11institutetext: Northeastern University, China
11email<EMAIL_ADDRESS>
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# What Have We Learned from OpenReview?

Gang Wang  Qi Peng  Yanfeng Zhang  Mingyang Zhang

###### Abstract

Anonymous peer review is used by the great majority of computer science
conferences. OpenReview is such a platform that aims to promote openness in
peer review process. The paper, (meta) reviews, rebuttals, and final decisions
are all released to public. We collect 5,527 submissions and their 16,853
reviews from the OpenReview platform. We also collect these submissions’
citation data from Google Scholar and their non-peer-reviewed versions from
arXiv.org. By acquiring deep insights into these data, we have several
interesting findings that could help understand the effectiveness of the
public-accessible double-blind peer review process. Our results can
potentially help writing a paper, reviewing it, and deciding on its
acceptance.

###### Keywords:

Peer review OpenReview Opinion divergence.

##  1 Introduction

Peer review is a widely adopted quality control mechanism in which the value
of scientific paper is assessed by several reviewers with a similar level of
competence. The primary role of the review process is to decide which papers
to publish and to filter information, which is particularly true for a top
conference that aspires to attract a broad readership to its papers. The
novelty, significance, and technical flaws are identified by reviewers, which
can help PC chair make the final decision.

Anonymous peer review (no matter single-blind or double-blind), despite the
criticisms often leveled against it, is used by the great majority of computer
science conferences, where the reviewers do not identify themselves to the
authors. It is understandable that some authors are uncomfortable with a
system in which their identities are known to the reviewers while the latter
remain anonymous. Authors may feel themselves defenseless against what they
see as the arbitrary behavior of reviewers who cannot be held accountable by
the authors for unfair comments. On the other hand, apparently, there would be
even more problems if letting authors know their reviewers’ identities.
Reviewers would give more biased scores for fear of retaliation from the more
powerful colleagues. Given this contradiction, opening up the reviews to
public seems to be a good solution. The openness of reviews will force
reviewers to think more carefully about the scientific issues and to write
more thoughtful reviews, since PC chairs know the identities of reviewers and
bad reviews would affect their reputations.

OpenReview111https://openreview.net/ is such a platform that aims to promote
openness in peer review process. The paper, (meta) reviews, rebuttals, and
final decisions are all released to public. Colleagues who do not serve as
reviewers can judge the paper’s contribution as well as judge the fairness of
the reviews by themselves. Reviewers will have more pressure under public
scrutiny and force themselves to give much fairer reviews. On the other hand,
previous works on peer-review analysis [16, 12, 21, 13, 10, 19] are often
limited due to the lack of rejected paper instances and their corresponding
reviews. Given these public reviews (for both accepted papers and rejected
ones), studies towards multiple interesting questions related to peer-review
are made available.

Given these public reviews, there are multiple interesting questions raised
that could help us understand the effectiveness of the public-accessible
double-blind peer review process: a) As known, AI conferences have extremely
heavy review burden in 2020 due to the explosive number of submissions [2].
These AI conferences have to hire more non-experts to involve in the double-
blind review process. How is the impact of these non-experts on the review
process (Sec. 3.1)? b) Reviewers often evaluate a paper from multiple aspects,
such as motivation, novelty, presentation, and experimental design. Which
aspect has a decisive role in the review score (Sec. 3.2)? c) The OpenReview
platform provides not only the submission details (e.g., title, keywords, and
abstract) of accepted papers but also that of rejected submissions, which
allows us to perform a more fine-grained cluster analysis. Given the fine-
grained hierarchical clustering results, is there significant difference in
the acceptance rate of different research fields (Sec. 3.3)? d) A posterior
quantitative method for evaluating papers is to track their citation counts. A
high citation count often indicates a more important, groundbreaking, or
inspired work. OpenReview releases not only the submission details of accepted
papers but also that of rejected submissions. The rejected submissions might
be put on arXiv.org or published in other venues to still attract citations.
This offers us opportunities to analyze the correlation between review scores
and citation numbers. Is there a strong correlation between review score and
citation number for a submission (Sec. 3.4)? e) Submissions might be posted on
arXiv.org before the accept/reject notification, which might be the rejected
ones from other conferences. They are special because they could be improved
according to the rejected reviews and their authors are not anonymous. Are
these submissions shown higher acceptance rate (Sec. 3.5)?

In this paper, we collect 5,527 (accepted and rejected) submissions and their
16,853 reviews from ICLR 2017-2020 venues222International Conference on
Learning Representations. https://iclr.cc/ on the OpenReview platform as our
main corpus. By acquiring deep insights into these data, we have several
interesting findings and aim to answer the above raised questions
quantitatively. Our submitted supplementary file also includes more data
analysis results. We expect to introduce more discussions on the effectiveness
of peer-review process and hope that treatment will be obtained to improve the
peer-review process.

##  2 Dataset

ICLR has used OpenReview to launch double-blind review process for 8 years
(2013-2020). Similar to other major AI conferences, ICLR adopts a reviewing
workflow containing double-blind review, rebuttal, and final decision process.
After paper assignment, typically three reviewers evaluate a paper
independently. After the rebuttal, reviewers can access the authors’ responses
and other peer reviews, and accordingly modify their reviews. The program
chairs then write the meta-review for each paper to make the final
accept/reject decision according to the three anonymous reviews. Each official
review mainly contains a review score (integer between 1 and 10), a reviewer
confidence level (integer between 1 and 5), and the detailed review comments.
The official reviews and meta-reviews are all open to the public on the
OpenReview platform. Public colleagues can also post their reviews on
OpenReview. We will present the collected dataset of submissions and reviews
from OpenReview, these submissions’ citation data from Google Scholar, and
their non-peer-reviewed versions from arXiv.org333These datasets and the
source code for the analysis experiment are available at
https://github.com/Seafoodair/Openreview/.

Table 1: Statistics of ICLR reviews dataset year |  #papers |  #authors |  accept rate |  #reviews |  review len.
---|---|---|---|---|---
2017 |  489 |  1,417 |  50.1% |  1,495 |  295.11
2018 |  939 |  2,882 |  49.0% |  2,849 |  372.07
2019 |  1,541 |  4,332 |  32.5% |  4,733 |  403.22
2020 |  2,558 |  7,765 |  26.5% |  7,766 |  407.08
total |  5,527 |  16,396 |  39.5% |  16,843 |  369.37

Submissions and Reviews. We have collected 5,527 submissions and 16,853
official reviews from ICLR 2017-2020 venues on the OpenReview platform. We
only use the review data since 2017 because the submissions before 2017 is too
few. Though a double-blind review process is exploited, the authors’
identities of the rejected submissions are also released after decision
notification. Thus, we can also access the identity information for each
rejected submission, which is critical in most of our analysis. Some
statistics of the reviews data are listed in Table 1, in which review len.
indicates the average length of reviews.

Citations. In order to investigate the correlation between review scores and
citation numbers, we also collect the citation information from Google Scholar
for all the 1,183 accepted papers from 2017 to 2019. Since the rejected
submissions might be put on arXiv.org or published in other venues, they might
also attract citations. We also collect the citation information for 2,054
rejected submissions that have been published elsewhere (210 for 2017, 324 for
2018, 493 for 2019, and totally 1027 rejected papers). All the citation
numbers are gathered up to 20 Jan. 2020. We do not collect citation
information of ICLR 2020 papers because they have not yet accumulated enough
citations yet.

arXiv Submissions. In order to investigate whether the submissions that have
been posted on arXiv.org before notification have a higher acceptance rate, we
also crawl the arXiv versions of ICLR 2017-2020 submissions if they exist. We
record the details of an arXiv preprint if its title matches an ICLR
submission title. Note that, their contents might be slightly different. We
totally find 1,158 matched arXiv papers and 948 among them were posted before
notification (178/150 for 2017, 103/79 for 2018, 420/303 for 2019, and 457/416
for 2020) up to 18 Feb 2020.

##  3 Results Learned from Open Reviews

###  3.1 How is the Impact of Non-Expert Reviewers?

Table 2: Statistics of different confidence level reviews | 2017-2019 | 2020
---|---|---
level1 | level2 | level3 | level4 | level5 | level1 | level2 | level3 | level4
#reviews | 74 | 455 | 2,330 | 4,612 | 1,600 | 1,104 | 2,554 | 2,659 | 1,449
fraction | 0.80% | 5.01% | 25.67% | 50.81% | 17.71% | 14.22% | 32.89% | 34.24% | 18.66%

Due to the extensively increasing amount of submissions, ICLR 2020 hired much
more reviewer volunteers. There were complaints about the quality of reviews
(47% of the reviewers have not published in the related areas [2]). Similar
scenarios have been observed in other AI conferences, such as NIPS, CVPR, and
AAAI. Many authors complain that their submissions are not well evaluated
because the assigned “non-expert” reviewers lack of enough technical
background and cannot understand their main contributions. How is the impact
of these “non-experts” on the review process? In this subsection, we aim to
answer the question through quantitative data analysis.

(a) 2017

(b) 2018

(c) 2019

(d) 2020

Figure 1: The review score distributions of different confidence level (conf)
reviews

Review Score Distribution. For ICLR 2017-2019, reviewer gives a review score
(integer) from 1 to 10, and is asked to select a confidence level (integer)
between 1 and 5. For ICLR 2020, reviewer gives a rating score in {1, 3, 6, 8}
and should select an experience assessment score (similar to confidence score)
between 1 and 4. We divide the reviews into multiple subsets according to
their confidence levels. Fig. 1 shows the smoothed review score distributions
for each subset of reviews. For 2018-2020, we consistently observe that the
scores of reviews with confidence level 1 and 2 are likely to be higher than
those reviews with confidence level 4 and 5. The trend of ICLR 2017 is not
clear because it contains too few samples to be statistically significant
(e.g., only 7 level-1 reviews). In 2017-2019, the lowest confidence level
reviews has an average review score 5.675, while the highest confidence level
reviews has an average review score 4.954. In 2020, the numbers for the lowest
and highest confidence level reviews are 4.726 and 3.678, respectively. Our
results show that the‘low-confidence reviewers (e.g., level 1 and 2) tend to
be more tolerant because they may be not confident about their decision, while
the high-confidence reviewers (e.g., level 4 and 5) tend to be more tough and
rigorous because they may be confident in the identified weakness.

(a) 2017

(b) 2018

(c) 2019

(d) 2020

Figure 2: The visualized layout of groups of reviews with different confidence
scores. Each point indicates a group of reviews with a specific confidence
level (abbrv. conf). The size of point indicates the relative number of
reviews in that group. The distance between two points indicates the
divergence of review scores between two groups.

Divergence Reflected by Euclidean Distance. On the other hand, peoples are
worrying that non-expert reviewers are not competent to give a fair evaluation
of a submission (e.g., fail to identify key contributions or fail to identify
flaws) and will ruin the reputation of top conferences [2]. Particularly,
these non-expert reviewers may have different opinions with the expert
reviewers regarding the same paper. Actually, opinion divergence commonly
exists between reviewers in the peer-review process. Each paper is typically
assigned to 3 reviewers. These 3 reviewers may have significantly different
review scores. In order to illustrate the difference between the reviews with
different confidence scores, we first compute the euclidean distance
$DIS(l_{i},l_{j})$ between between group $l_{i}$ and group $l_{j}$ as follows.
Let $R_{l_{i},l_{j}}$ be the set of paper IDs, where each paper concurrently
has both confidence-$l_{i}$ review(s) and confidence-$l_{j}$ review(s). Let
$\overline{s_{p}^{i}}$ be paper $p$’s average review score from
$l_{i}$-confidence reviews. Then, the distance between the group of
confidence-$l_{i}$ reviews and that of confidence-$l_{j}$ reviews is:

$DIS(l_{i},l_{j})=\sqrt{\sum_{p\in
R_{l_{i},l_{j}}}\Big{(}\overline{s_{p}^{i}}-\overline{s_{p}^{j}}\Big{)}^{2}}.$
(1)

After computing the distance betwenn each pair of groups, we can construct a
distance matrix. According to the distance matrix, we use t-SNE [14] to plot
the visualized layout of different groups of reviews of each year in Fig. 2.
For ICLR 2017-2019, we can see similar layout, where conf1 reviews are close
to conf2 reviews in the central part and the groups of conf3, conf4, and conf5
locate around. The group of conf4 reviews is far apart from the most
professional reviews (conf5). In ICLR 2020, there are 4 confidence levels.
Surprisingly, we observe that the most professional reviews (conf4) and least
professional reviews (conf1) are closest to each other. Conf2 reviews and
conf3 reviews are both far apart from conf4 reviews.

(a) 2017-2019

(b) 2020

Figure 3: MJS divergence of different combinations of different confidence
level reviews

Divergence Reflected by Jensen-Shannon Divergence. By using euclidean
distance, we can only measure the divergence of two sets of different level
reviews. Inspired by Jensen-Shannon Divergence for multiple distributions
(MJS) [4], we design the MJS metric to measure the divergence between multiple
sets of reviews. The MJS of $m$ sets ($m\geq 2$) of different confidence
reviews is defined as follows:

$MJS(l_{1},\ldots,l_{m})=\frac{1}{m}\sum_{i\in\\{l_{1},\ldots,l_{m}\\}}\Bigg{(}\frac{1}{|R_{l_{1},\ldots,l_{m}}|}\sum_{p\in
R_{l_{1},\ldots,l_{m}}}\overline{s_{p}^{i}}\cdot
log\Big{(}\frac{\overline{s_{p}^{i}}}{\overline{s_{p}^{[1,m]}}}\Big{)}\Bigg{)},$
(2)

where $R_{l_{1},\ldots,l_{m}}$ is the set of paper IDs, where each paper
concurrently has reviews with confidence levels $l_{1},\ldots,l_{m}$,
$|\cdot|$ returns the size of a set, $\overline{s_{p}^{i}}$ is paper $p$’s
average review score of $l_{i}$-confidence reviews, and
$\overline{s_{p}^{[1,m]}}$ is paper $p$’s average review score of reviews with
confidence levels $l_{1},\ldots,l_{m}$. The bigger the $MJS$ is, the
significant the opinion divergence is. A nice property of MJS metric is that
it is symmetric, e.g., $MJS(i,j)=MJS(j,i)$ and $MJS(i,j,k)=MJS(k,j,i)$. We
measure the MJS divergence of different combinations of confidence levels and
show the results in Fig. 3. Note that, the results of combinations that
contain less than 10 reviews are not shown since they are too few to be
statistically significant. In 2017-2019 the MJS divergence between conf1
reviews and conf5 reviews is the smallest. In 2020, it shows bigger divergence
than 2017-2019 on different combinations but relatively similar divergence
results among different combinations. In addition, a combination of three
different confidence levels is likely to result in bigger divergence than a
combination of two confidence levels.

How is the Impact of Non-Expert Reviewers? All these facts demonstrate that
the opinion divergence is not greater after introducing more non-expert
reviewers. We also observe that the opinion divergence between non-expert
reviewers and other reviewers is often relatively small. The reason behind
might be that the non-expert reviewers often have a more neutral opinion
rather than clear yes or no. They are more cautious to give positive or
negative recommendations.

###  3.2 Which Aspects Play Important Roles in Review Score?

Reviewers often evaluate a paper from various aspects. There are five most
important aspects, i.e., novelty, motivation, experimental results,
completeness of related workers, and presentation quality. Some conferences
provide a peer-review questionnaire that requires reviewer to evaluate a paper
from various aspects and give a score with respect to each aspect.
Unfortunately, ICLR does not ask reviewers to answer such a questionnaire.
Then a question arises accordingly. Which aspects play more important roles in
determining the review score? We aim to answer this question by analyzing the
sentiment of each aspect.

Corpus Creation. For each review, we first extract the related sentences that
describe different aspects by matching a set of predefined keywords. The
keywords “novel, novelty, originality, and idea” are used to identify a
sentence that describes novelty of the paper, “motivation, motivate, and
motivated” are used to identify a sentence related to motivation,
“experiments, empirically, empirical, experimental, evaluation, results, data,
dataset, and data set” are used to identify a sentence related to experiment
results, “related work, survey, review, previous work, literature, cite, and
citation” are used to identify a sentence related to the completeness of
related work, and “presentation, writing, written, structure, organization,
structured, and explained” are used to identify a sentence related to
presentation quality. We have collected a corpus containing 95,208 sentences
which are divided into five subsets corresponding to the five aspects.
Specifically, we have 11,916 sentences related to “novelty”, 5,107 sentences
related to “motivation”, 62,446 sentences related to “experimental results”,
8,710 sentences related to “completeness of related work”, and 7,029 sentences
related to “presentation quality”.

Automatic Annotation. In order to train a sentiment analysis model, we need to
first annotate enough number of sentences with sentiment label (i.e.,
positive, negative, and neutral). However, this workload of manual annotation
is huge due to the large size of review corpus. Fortunately, we find a
possibility of automatic annotation after analyzing the reviews. A large
number of reviewers write their positive reviews and negative reviews
separately by using the keywords such as “strengths/weaknesses”, “pros/cons”,
“strong points/weak points”, “positive aspects/negative aspects”, and so on.
We segment the review text and identify the positive/negative sentences by
looking up these keywords. The boundaries are identified when meeting an
opposite sentiment word for the first time. By intersecting the set of
positive/negative sentences with the set of aspect-specific sentences, we
obtain a relatively large set of sentiment-annotated corpus for each aspect.
Particularly, we have 2,893 sentiment-annotated sentences for “novelty”, 1,057
for “motivation”, 8,956 for “experimental results”, 1,402 for “completeness of
related work”, 1,644 for “presentation quality”, and 15,952 in total. We also
manually annotate 6,095 sentences including 1,227 corrected automatically
annotated sentences since some neutral sentences are incorrectly annotated
with positive or negative sentiment. Finally, we have 20,820 labeled
sentences444All of the annotated data including manually annotated ones are
publicly available at at https://github.com/Seafoodair/Openreview/., i.e.,
21.87% of the total number of sentences (95,208) in corpus. Note that, there
might be more than one sentences describing one aspect but having different
sentiments. In such a case, we label the sentiment by a majority vote.

Sentiment Analysis. Given these five datasets including the labeled data, we
perform sentiment analysis for each aspect using a pretrained text model
ELECTRA [7] which was recently proposed in ICLR 2020 with state-of-the-art
performance. The detailed hyper-parameter settings of ELECTRA are described in
our support materials. We split the annotated dataset of each aspect into
training/validation/test sets (8:1:1), and use 10-fold cross validation to
train five sentiment prediction models for the five aspects. We obtain five
accuracy results 93.96%, 88.46%, 94.99%, 85.12%, and 93.38% for novelty,
motivation, experimental results, completeness of related workers, and
presentation quality, respectively. We then use the whole annotated dataset of
each aspect to train the corresponding sentiment analysis model and use this
model to predict the sentiment of the other unlabeled sentences of each
aspect. Finally, for each review, we can obtain the sentiment score of each
aspect. Note that, some individual aspects might not be mentioned in a review,
which are labeled with neutral.

Figure 4: The sentiment of each aspect vs. the review score. Each column
represents a group of reviews with the same combination of aspect sentiments.
These groups are sorted in the descending order of the average review score of
a group of reviews.

Sentiment of Each Aspect vs. Review Score. Given the sentiment analysis
results of all aspects of each review and the review score, we perform the
correlation analysis. We group the reviews with the same combination of aspect
sentiments and compute the average review score of each group. The groups that
receive less than 3 reviews are not considered since they have too few samples
to be statistically significant. We visualize the result as shown in Fig. 4.
We can see that the higher review score often comes with more positive aspects
from a macro perspective, which is under expectation. We observe that most of
the reviews with score higher than 6 do NOT have negative comments on novelty,
motivation, and presentation, but may allow some flaws in related work and
experiment. The reviewers that have overall positive to the paper are likely
to pose improvement suggestions on related work and experiment to make the
paper perfect. The presentation quality and experiment seem to be mentioned
more frequently than the other aspects, and the positive sentiment on
presentation is distributed more evenly from high-score reviews to low-score
reviews. This implies that presentation does not play important role in making
the decision. It is also interesting that there is no review in which all
aspects are positive or negative. It is unlikely that a paper is perfect in
all aspects or has no merit. Reviewers are also likely to be more rigorous in
‘ papers and be more tolerant with poor papers.

Causality Analysis. In order to explore which aspect determines the final
review score, we perform causal inference following [9]. Besides the above
five aspects, we also include the factor of reviewer confidence. The process
of causal analysis includes four steps: modeling, intervention, evaluation,
and inference. In the modeling process, we use multivariate linear regression
method[3] to perform regression task on the ICLR reviews dataset, where the
six evaluated parameters are the sentiment scores of the five review aspects
and a reviewer confidence score, and the regression label is the review score.
Each parameter is standardized to [-1,1]. To avoid randomness of model
training, we launch 1000 times of training and obtain the average MSE (Mean
Square Error) 0.24. The intervention process removes each factor $x$ one by
one and performs multiple times of model evaluation to obtain multiple average
MSE results, each corresponding to an $x$-absence model. In the absence of
overfitting, the MSE value of any $x$-absence model should be larger than
0.24. The MSE value of the $x$-absence model implies the causality. A larger
MSE value of an $x$-absence model implies that the factor $x$ is more dominant
in determining the final score, and vice versa. In the inference process, we
compare the MSE values to infer the causality. The average MSE values of the
reviewer confidence, novelty, motivation, experiment, related work, and
presentation are 0.84, 0.77, 0.34, 0.86, 0.33, and 0.34, respectively. We
observe that the factors of reviewer confidence, novelty, and experiment
change the MSE greatly, so they are more dominant in determining the final
score.

###  3.3 Which Research Field has Higher/Lower Acceptance Rate?

AI conferences consider a broad range of subject areas. Authors are often
asked to pick the most relevant areas that match their submissions. Area chair
could exist who makes decisions for the submissions of a certain research
area. Different areas may receive different number of submissions and also may
have different acceptance rates. Program chairs sometimes announce the number
of submissions and the acceptance rate of each area in the opening event of a
conference, which could somehow indicate the popularity of each area. But, the
classification by areas is coarse. A more fine-grained classification that
provides more specific information is desired. Thanks to the more detailed
submission information provided by OpenReview, we utilize the title, abstract,
and keywords of each submission to provide a more fine-grained clustering
result and gather the statistics of acceptance rate of each cluster of
submissions.

We first concatenate the title, abstract, keywords of each ICLR 2020
submission and preprocess them by removing stop words, tokenizing, stemming
list, etc. We leverage an AI terminology dictionary [1] during the tokenizing
process to make sure that an AI terminology containing multiple words is not
split. We then formulate term-document matrix (i.e., AI term-submission
matrix) by applying TF-IDF and calculate cosine distance matrix. The size of
the term-document TF-IDF matrix for ICLR 2020 is 12436 x 2558, and the size of
the cosine distance matrix is 2558 x 2558. We then apply the Ward clustering
algorithm [11] on the matrix to obtain submission clusters. Ward clustering is
an agglomerative hierarchical clustering method, meaning that at each stage,
the pair of clusters with minimum between-cluster distance are merged. We use
silhouette coefficient to finalize the number of clusters and plot a
dendrogram to visualize the hierarchical clustering result as shown in Fig. 5.

Figure 5: Visualized hierarchical clustering result of ICLR 2020 submissions.
Each leaf node represents a submission. Cosine distance 5 is selected as the
threshold to control the granularity of leaf-level clusters. There are 99
clusters in total, including both fine-grained clusters and coarse-grained
clusters. Clusters are numbered in the order of their acceptance rate. The
color of keywords indicates the acceptance rate of that cluster. Light green
means a high acceptance rate, while light red means a low acceptance rate. The
keywords of some typical clusters are labeled.

From Fig. 5, we observe three aspects of insights. (a) Overall Structure of
Deep Learning Research. We observe the correlation between research topics.
For example, the submissions in the left part belong to reinforcement learning
field (20), which is far apart from all the other research topics (because it
is the last merged cluster and its distance to the other clusters is more than
27). Another independent research field is Graph Neural Networks (GNNs) (49),
as a promising field, becomes really hot in only 2-3 years, which
distinguishes itself from others by focusing on graph structure. Adversarial
Machine Learning (31) is also an independent research field that attempts to
fool models through malicious input and different from others. The next
independent subject is Generative Adversarial Networks (GANs) (80). But GANs
is not completely independent since we found that many submissions on NLP (36)
and CV (75) are mixed with GANs as well. We also observe that Transfer
Learning (72) is close to GANs, since some works have applied transfer
learning to GANs. Most of the submissions in the right part are applications
related (e.g., vision, audio, NLP, biology, chemistry, and robotics). They are
mixed with DNN optimization techniques since many optimizations are proposed
to improve DNN on a specific application field. (b) Popularity Difference
between Clusters. We observe that multiple areas attract large amount of
submission. For example, Reinforcement Learning (20), GNNs (49), GANs (80),
NLP (36), and Computer Vision (75) have attracted more than 50% of the
submissions, which are really hot topics in today’s deep learning research.
(c) Acceptance Rate Difference between Clusters. There exists significant
difference on acceptance rate between clusters, say ranging from 53.33% to
10.53%. The cluster of submissions on “Black-Box Adversarial Attacks” has the
highest acceptance rate (53.33%), which is a subject belongs to “Adversarial
Machine Learning” area. The top-6 highest acceptance rate topics are listed in
the figure. The cluster of submissions on “Few-Shot Learning” has the lowest
acceptance rate (10.53%), which is a subject belongs to “Reinforcement
Learning” area. The top-5 lowest acceptance rate topics are listed in the
figure. We also list some typical topics in the figure. For example, the
cluster on “Graph Neural Networks (49)” has an acceptance rate of 26.67%. The
cluster on “BERT (38)” has an acceptance rate of 27.27%. The cluster on “GANs
(80)” has an acceptance rate of 20.18%. The cluster on “Reinforcement Learning
(20)” has an acceptance rate of 31.58%.

###  3.4 Review Score vs. Citation Number

The citation number quantitatively indicates a paper’s impact. In this
subsection, we show several interesting results on the correlation between
review scores and citation numbers.

Is There a Strong Correlation between Review Score and Citation Number?
OpenReview releases not only the submission details and reviews of the
accepted papers but also that of the rejected submissions. These rejected
submissions might be put on arXiv.org or published in other venues and still
make an impact. We collect the citation number information of both accepted
papers and rejected papers and study the correlation between their review
scores and their citation numbers. We plot the histogram of average citation
numbers of ICLR 2017-2019 submissions as shown in Fig. 6. The papers are
divided into multiple subsets according to their review scores. Each bin of
the histogram corresponds to a subset of papers with similar review scores
(with an interval of 0.3). Then the average citation number of each subset is
calculated. The color of bin indicates the acceptance rate of the
corresponding subset of papers. From the figure, we can observe that the
papers with higher review score are likely to have higher citation numbers,
which is under expectation.

(a) 2017

(b) 2018

(c) 2019

Figure 6: The histogram of citation numbers against 0.3-intervals of average
review score

(a) 2017

(b) 2018

(c) 2019

Figure 7: The distribution of citation numbers of individual papers, where the
papers on the x-axis are sorted in the ascending order of their review scores

We further investigate the citation numbers of individual papers as shown in
Fig. 7. Each point represents a paper. Green color indicates an accepted paper
and red color indicates a rejected one. The papers are sorted on the x-axis in
the ascending order of their review scores. The distribution of citation
numbers is messy. We can see that many rejected papers gain a large number of
citations (i.e, red points in the top-left part), which is a bit surprised.
Generally speaking, the accepted papers will attract more attentions since
they are officially published in ICLR. However, the rejected papers may be
accepted later at other venues and still attract attentions. In addition, a
few papers with high review score are rejected (i.e., red points on the right
side). We observe that the reject decision does not impact their citation
numbers. Though rejected, the papers with higher review score are still likely
to have higher citation numbers.

Figure 8: Review score variance of a paper vs. average citation number

Do Great Papers Gain More Diverse Review Scores? People always have diverse
opinions on the breakthrough works. Reviewer A thinks it novel and is happy to
give higher scores, but reviewer B may think it too crazy or unrealistic and
reject it. There might be a big debate between reviewers. But it is usually
hard to reach consensus. We investigate the relationship between the variance
of review scores of a submission and its citation number. We group papers
according to their review score variances and calculate the average citation
number of each group. Fig. 8 shows the statistical results of the submissions
of ICLR 2017-2019. We observe that the papers that have large number of
citations are indeed more likely to gain diverse review scores. Note that a
paper that has diverse review scores (big review score variance) does not
necessarily have high review scores.

###  3.5 Do Submissions Posted on arXiv have Higher Acceptance Rate?

We found 1,083 submissions that have been posted on arXiv before accept/reject
notification555We compare paper creation date on arXiv with ICLR official
notification date., which account for about 19.59% of the total submissions.
The arXiv versions are not anonymous, which bring unfairness to the double-
blind review process. We refer to the submissions that have been posed on
arXiv before notification as “arXived submissions”. We investigate the
acceptance rates of the arXived and non-arXived submissions. The acceptance
rates of the arXived submissions in 2017, 2018, 2019, and 2020 are 59.33%,
62.39%, 45.36%, and 30.48%, respectively. The acceptance rates of the non-
arXived submissions in 2017, 2018, 2019, and 2020 are 45.88%, 41.23%, 26.37%,
and 17.22%, respectively. We observe that the arXived submissions have
significantly higher acceptance rate than the non-arXived submissions (49.39%
vs. 32.68% on average).

(a) 2017

(b) 2018

(c) 2019

(d) 2020

Figure 9: The number of accepted/rejected arXived submissions posted on arXiv
by month. SMT means the submission deadline. NT means the notification date.
CONF means the conference date.

We think the reason should be not only anonymity but also that the arXived
ICLR submissions have higher quality. These arXived submissions might attract
more feedbacks from colleagues, according to which the authors can improve
their manuscripts. The arXived submissions might also be the rejected ones
from other conferences and might have been improved according to the rejection
reviews. We also observe that some arXived submissions are posted on arXiv one
year before the submission deadline. Fig. 9 shows the number of arXived
submissions posted on arXiv by month, including both accepted ones and
rejected ones. We can see that the papers posted on arXiv are more and more
when approaching the submission deadline. There are also a large number of
papers posted on arXiv between the submission date and the notification date.
From the aspect of acceptance rate, we observe that the earlier the papers are
posted on arXiv, the more likely they are accepted. In addition, the papers
posted on arXiv after notification date have a higher acceptance rate. The
reason might be that the authors cannot wait to share their research results
after their papers are accepted.

##  4 Related Work

There exist many interesting works related to peer-review analysis. We list
several related works as follows.

Ivan Stelmakh’s Blog [18] shares a lot of interesting findings: First,
reviewers give lower scores once they are told that a paper is a resubmission.
Second, there is no evidence of herding in the discussion phase of peer
review. Third, A combination of the selection and mentoring mechanisms results
in reviews of at least comparable and on some metrics even higher-rated
quality as compared to the conventional pool of reviews. Nihar B.Shah et al.
[17] analyzed the influence of reviewer and AC bid, reviewer assignment,
different types of reviewers, rebuttals and discussions, distribution across
subject areas in detail. Homanga Bharadhwaj et al. [5] provide an analysis on
whether there is a positive impact if his/hers paper is upload on arXiv before
the submission deadline. They suggest that the paper arXived will have a
higher acceptance rate. David Tran et al. [20] analyzed ICLR conferences and
quantified reproducibility/randomness in review scores and acceptance
decisions, and examined whether scores correlate with paper impact. Their
results suggest that there exists strong institutional bias in accept/reject
decisions, even after controlling for paper quality. They analyzed the
influence of scores among gender, institution, scholar reputation in detail.
Emaad Manzoor et al. [15] proposed a framework to nonparametrically estimate
biases expressed in text. The authors leveraged the framework to accurately
detect these biases from the review text without having access to the review
ratings. Birukou et al. [6] analyzed ten CS conferences and found low
correlation between review scores and the impact of papers in terms of future
number of citations. Gao et al. [8] predict after-rebuttal (i.e., final)
scores from initial reviews and author responses. Their results suggest that a
reviewer’s final score is largely determined by her initial score and the
distance to the other reviewers’ initial scores. Li et al. [13] utilize peer
review data for the citation count prediction task with a neural prediction
model.

In this paper, we investigate ICLR 2017-2020’s submissions and reviews data on
OpenReview and show more different interesting results, e.g., the effect of
low confidence reviews, the sentiment analysis of review text on different
aspects, the hierarchical relationships of different research fields, etc,
which have not been studied before.

##  5 Conclusion

We perform deep analysis on the dataset including review texts collected from
OpenReivew, the paper citation information collected from GoogleScholar, and
the non-peer-reviewed papers from arXiv.org. All of these collected data are
publicly available on Github, which will help other researchers identify novel
research opportunities in this dataset. More importantly, we investigate the
answers to several interesting questions regarding the peer-review process. We
aim to provide hints to answer these questions quantitatively based on our
analysis results. We believe that our results can potentially help writing a
paper, reviewing it, and deciding about its acceptance.

## References

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# The Hydra Hand: A Mode-Switching Underactuated Gripper with Precision and
Power Grasping Modes

Digby Chappell, Fernando Bello, Petar Kormushev, and Nicolas Rojas Manuscript
received: July 5, 2023; Revised: August 30, 2023; Accepted: September 22,
2023.This paper was recommended for publication by Editor Hong Liu upon
evaluation of the Associate Editor and Reviewers’ comments.Digby Chappell,
Fernando Bello, Petar Kormushev, and Nicolas Rojas are with Imperial College
London, London SW7 2BX, UK. Contact<EMAIL_ADDRESS>This work
was supported in part by the UKRI CDT in AI for Healthcare http://ai4health.io
(Grant No. EP/S023283/1).For the purpose of open access, the author(s) has
applied a Creative Commons Attribution (CC BY) license to any Accepted
Manuscript version arising.Digital Object Identifier (DOI): see top of this
page.

###### Abstract

Human hands are able to grasp a wide range of object sizes, shapes, and
weights, achieved via reshaping and altering their apparent grasping stiffness
between compliant power and rigid precision. Achieving similar versatility in
robotic hands remains a challenge, which has often been addressed by adding
extra controllable degrees of freedom, tactile sensors, or specialised extra
grasping hardware, at the cost of control complexity and robustness. We
introduce a novel reconfigurable four-fingered two-actuator underactuated
gripper—the Hydra Hand—that switches between compliant power and rigid
precision grasps using a single motor, while generating grasps via a single
hydraulic actuator—exhibiting adaptive grasping between finger pairs, enabling
the power grasping of two objects simultaneously. The mode switching mechanism
and the hand’s kinematics are presented and analysed, and performance is
tested on two grasping benchmarks: one focused on rigid objects, and the other
on items of clothing. The Hydra Hand is shown to excel at grasping large and
irregular objects, and small objects with its respective compliant power and
rigid precision configurations. The hand’s versatility is then showcased by
executing the challenging manipulation task of safely grasping and placing a
bunch of grapes, and then plucking a single grape from the bunch.

###### Index Terms:

Grasping; Multifingered Hands; Mechanism Design

##  I Introduction

The grasping versatility of human hands is unparalleled by any other natural
or mechanical gripper. Human hands are the result of a complex system of motor
and sensory pathways that enable humans to switch at ease between precision
and power grasps, and offering compliance and adaptability to varying shapes,
sizes, and weights [1]. To achieve this in a robotic gripper is extremely
challenging, with grasped object variability, unstructured grasping
environments, multi-contact sensing difficulties, and high occlusion
manipulation all contributing towards complexity. Achieving anywhere near
human-level versatility with one gripper and a reduced number of actuators is
non-trivial, and indeed finding an optimal method of doing so remains an open
area in robot manipulation research.

Figure 1: The Hydra Hand, capable of performing rigid precision and compliant
power grasps with a single hydraulic grasp actuator, where mode-switching is
achieved via a rotating palm. (a) Two-fingered precision grasping mode. (b)
Four-fingered spherical grasping mode. (c) Rigid precision palmar pinch grip.
(d) Cylindrical power grip with adaptability between pairs of fingers. (e)
Spherical power grip with individual finger compliance.

Many works have approached the grasping versatility problem from a control and
sensory perspective. Often, tactile sensors [2] and vision systems [3] are
used, or computational models of the interaction between the gripper and known
objects are developed [4]. These studies display impressive dexterity with
known objects, but are limited in robustness and how well they generalise to
grasping unknown and varied objects. Furthermore, grasping in unstructured
environments is difficult from a sensory perspective, where high occlusion and
multiple contact points with objects increases complexity significantly.

Figure 2: A comparison between the dimensions and grasping actions of the
Hydra Hand and a human hand. Key dimensions of the Hydra Hand (a) are modelled
on corresponding dimensions of a human hand (b). The Hydra Hand (c) is able to
imitate human grasps (d), specifically those involving form-closure and palmar
pinch grips.

A related approach is to increase the number of degrees of freedom (DOFs) in
the gripper such that an increased number of grasps can be achieved. Many
anthropomorphic hands utilise this strategy, such as the ILDA hand [5] and the
Robonaut Hand [6]. Some non-anthropomorphic hands, such as the farmHand [7]
and the Yale Model W [8] also follow this trend, with targeted DOFs for
desired grasps. However, increasing the number of controllable DOFs increases
control complexity, and ensuring constraints such as grasp success and safety
are satisfied can be difficult with a larger action space.

Rather than increasing gripper complexity, an alternative method is to
optimise a small number of DOFs to achieve a wider range of manipulation
tasks. These reduced-complexity grippers are usually designed for in-hand
manipulation, with specialised DOFs for reorienting and translating grasped
objects. Common design features include variable friction surfaces [9, 10] and
the ability to reconfigure the fingers of the gripper [11, 12, 13, 14]. These
works show robust in-hand manipulation results with a range of grasped
objects, however these grasped objects are often limited to larger diameters
with compliant grasps. Achieving compliance with a small number of degrees of
freedom requires underactuation, where a small number of control inputs
actuate multiple joints indirectly. The most common technique of achieving
compliance in this way is to have a single tendon routed through multiple
joints, as in the RUTH gripper [13] and the OLYMPIC hand [15]. Reducing the
number of actuators even further is often achieved with a differential
mechanism in the palm of the hand that allows non-contacting fingers to
continue flexing when one becomes blocked. Examples of these are often present
in prostheses, where mechanical and control complexity must be minimised, such
as the Hannes hand [16], which uses a series of balanced pulleys, or the soft
prosthesis presented in [17], which is pneumatically actuated such that
actuation pressure is distributed across the finger. However, these hands are
ill-suited to precision tasks, being designed primarily for power grasps.

Reduced-order grippers designed for both compliant and precise grasps are much
rarer. The Meso gripper uses a single actuator to achieve power and pinch
grasps with a two-fingers [18], however the Meso gripper’s contact region is
limited to its fingertips, while the remainder of each finger is a fully
actuated mechanism. Adaptability in this case is achieved via a differential
pulley in the palm, and precision grasping relies on zero surface contact
before the grasp — something that is difficult to achieve when grasping small
objects. The Velo gripper [19], also a two-fingered single-actuator gripper,
uses underactuation to its advantage in precision grasping. Each finger of the
Velo gripper is balanced with an extension tendon that causes the distal
phalanges to form a parallel grasp by default, meaning that an enveloping
grasp—a necessity for heavier objects—only occurs when an object is actively
blocking the proximal phalanges. A significant drawback of both the Meso and
the Velo grippers is that they are two-fingered, restricting grasping to a
single axis and reducing their form-closure ability. As such, the use of
minimal actuators for multi-fingered precision and compliance is a potentially
fruitful avenue of research that is presently unexplored.

In this work, we present the Hydra Hand (shown in Fig. 1): a four-fingered
gripper that is capable of switching between power and precision modes, and is
operated by a single grasping actuator and a single switching actuator. By
utilising McKibben muscles to drive fingers in pairs, pressure generated from
a single input is shared across four fingers, allowing compliance and
independent motion of finger pairs during power grasping. Deliberately
misaligned flexion axes of adjacent fingers allow the hand to switch modes
from four underactuated fingers for compliant grasping to two rigid fingers
for precision grasping with a palm rotation mechanism. We test the gripper on
a range of household benchmarking objects and textiles, and find that it
excels at both power and precision grasps, and can adapt its grip to
accommodate objects of varying size and shape. We demonstrate the versatility
of the gripper in a challenging manipulation scenario: grasping a bunch of
grapes, then picking a single grape from the bunch.

##  II Design

The Hydra Hand is designed to perform both rigid precision and compliant power
grasps. The targeted precision grasp is a two-fingered pinch, which can take
the form of a palmar pinch or an inferior pincer [20]. For power grasps,
reshaping the hand and caging objects in three dimensions becomes more
important, so both cylindrical and spherical power multi-finger grasps are
targeted. Furthermore, the shape of power-grasped objects can be irregular,
therefore a level of adaptability between compliant fingers is necessary to
accommodate for this. The design of the Hydra Hand is split into three
subsections: the mode-switching mechanism, the kinematics of the gripper, and
finger actuation.

The dimensions of the Hydra Hand are modelled on those of an adult male’s hand
(see Fig. 2). Each finger is approximately $95$ mm long and has a width and
thickness of $20$ mm, falling in the range of the average index finger length
and average thumb width and thickness. The palm of the Hydra Hand has a
diameter of $60$ mm, which is designed to match the average distance from the
base of the thumb to the base of the index finger on the palmar surface of a
human hand. At rest the fingers are at an angle of $30^{\circ}$ to each other,
giving a fingertip-to-fingertip distance of approximately $140$ mm, mimicking
the angle and fingertip-to-fingertip distance of a human hand when reaching to
grasp a medium-sized object. The hand is comprised of four fingers: two type
$A$ fingers, and two type $B$ fingers (shown in Fig. 3); the length of the
proximal and distal phalanges of the type $A$ fingers are $50$ mm and $45$ mm,
respectively, and of the type $B$ fingers are $40$ mm and $55$ mm,
respectively.

Figure 3: Schematic diagram of the fingers and joints of the Hydra Hand. (a)
Individual $A$ and $B$ fingers and the combined $A+B$ fingers. (b) Joints of
each finger - note that all fingers are designed with the same tendon routing
offsets and initial angular offsets. Flexion tendons are shown in red on the
palmar side of each finger. Extension elastic cords are shown in blue on the
dorsal side of each finger.

###  II-A Mode-Switching

The type $A$ fingers of the Hydra Hand are mounted on a static palm section,
while the type $B$ fingers are mounted on a rotating palm section. The
rotating palm section is actuated by a stepper motor, and allows the hand to
reconfigure and reshape to achieve a range of grasps between cylindrical and
spherical grips. When the rotating palm reaches its limit, adjacent fingers
magnetically lock together and produce a precision gripper. The base of each
finger is deliberately mounted at a lateral offset from the sagittal plane of
the hand, meaning the flexion planes of all four fingers are parallel when the
hand is in precision mode. A similar rotating palm is utilised in the Yale
Model Q, but is primarily used to perform in-hand rotation of objects, rather
than to expand the available grasps of the gripper [21].

Unlike a human finger, that can flex its proximal and distal joints
independently, the Hydra Hand is underactuated, with a single tendon
controlling both finger joints. Because it is not possible to purposely
position the fingertips such that a fingernail feature on the dorsal side of
the finger could be used for a tip pinch grip, the targeted precision grasps
are the palmar pinch and inferior pincer grasps. To aide this, a fingernail
feature is present on the distal face of the fingertip, allowing the palmar
pinch grasp to also serve as a tip pinch.

Figure 4: The finger locking mechanism of the Hydra Hand. (a) and (c), Flexion
axes of the distal joints of each finger are misaligned. (b) and (d) Locating
magnetic features embedded in each finger prevent relative in-plane rotation
and translation of laterally adjacent links.

The motion group of finger $A$ can be described by sequential rotations around
joint axes $a_{1}$ and $a_{2}$ [22]:

$\mathcal{S}_{A}=\\{\bm{R}(A_{1},a_{1})\\}\cdot\\{\bm{R}(A_{2},a_{2})\\},$ (1)

and similarly for finger $B$, with joint axes $b_{1}$ and $b_{2}$:

$\mathcal{S}_{B}=\\{\bm{R}(B_{1},b_{1})\\}\cdot\\{\bm{R}(B_{2},b_{2})\\}.$ (2)

When the rotating palm section reaches its limit, the finger pairs are aligned
and passively locked together with raised features and magnetic contacts (see
Fig. 4) to act as a single combined finger for two-fingered grasping. To
switch from compliant fingers to rigid fingers for precision grasping, the
distal flexion axes of the fingers are misaligned. The raised features prevent
the relative in-plane motion of the adjacent distal links, and therefore
constrain the flexion of the distal joints to be zero. The only joint that can
therefore be actuated is the combined proximal joint, producing a two-fingered
rigid gripper that can perform precision grasps. When finger $A$ and $B$ are
combined, the combined finger motion group is the intersection of the two
component motion groups,
$\mathcal{S}_{A+B}=\mathcal{S}_{A}\cap\mathcal{S}_{B}$:

$\displaystyle\mathcal{S}_{A+B}$
$\displaystyle=(\\{\bm{R}(A_{1},a_{1})\\}\cdot\\{\bm{R}(A_{2},a_{2})\\})\cap$
$\displaystyle(\\{\bm{R}(B_{1},b_{1})\\}\cdot\\{\bm{R}(B_{2},b_{2})\\}).$ (3)

The laterally adjacent links of $A$ and $B$ are rigidly connected, and axes
$a_{1}$ and $b_{1}$ are aligned, therefore
$\\{\bm{R}(A_{1},a_{1})\\}=\\{\bm{R}(B_{1},b_{1})\\}$ and
$\mathcal{S}_{A+B}=\\{\bm{R}(A_{1},a_{1})\\}(\\{\bm{R}(A_{2},a_{2})\\}\cap\\{\bm{R}(B_{2},b_{2})\\})$.
Since $a_{2}$ and $b_{2}$ are parallel but offset,
$\\{\bm{R}(A_{2},a_{2})\\}\cap\\{\bm{R}(B_{2},b_{2})\\}=\\{\bm{I}\\}$ is
identity displacement. The combined motion group of displacement is therefore:

$\mathcal{S}_{A+B}=\\{\bm{R}(A_{1},a_{1})\\}.$ (4)

In the direction perpendicular to the flexion plane of the fingers, the
fingers are only held in contact by the attractive forces of the magnets;
these forces are not large and allow the fingers to be unlocked by reversing
the direction of the rotating palm unit. This simple mechanism allows the hand
to switch from compliant power grasps to rigid precision grasps and vice versa
without any dedicated hardware changes.

Figure 5: Joint space of an individual finger, with joint limits and lines of
constant tendon length shown. Lines of constant tendon length are coloured
according to the amount of stored elastic potential energy in the elastic
extension cord, with lighter showing less energy, and darker showing more
energy (normalised per line). The minimum-energy unhindered joint-space
trajectory of a finger is shown from rest ($-\frac{\pi}{6}$, $0$) to maximum
flexion ($\frac{\pi}{3}$, $\frac{\pi}{2}$) in red.

###  II-B Gripper Kinematics

During power grasping, the fingers of the hand are underactuated, with a
single tendon driving two flexion joints of each finger, and elastic cord
passively driving the extension of both joints (see Fig. 3 (a)). The tendon is
routed inside each finger such that it directly acts on each joint, rather
than being routed around a joint pulley. The underactuation of each finger
enables the hand to be compliant—if the proximal joint gets blocked then the
distal joint is free to continue closing. The retractable tendon length
$L_{f,j}^{(t)}$ of joint $j$ of finger $f$ can be found using the cosine rule
with joint angle $q_{f,j}$ and, with reference to Fig. 3(b), tendon routing
points at perpendicular offsets of $d_{j,l}^{(t)}$ on link $l$ and
$d_{j,l-1}^{(t)}$ on the preceding link, with an offset angle of
$q_{j}^{(0)}$:

$L_{f,j}^{(t)}=\sqrt{d_{j,l}^{(t)^{2}}+d_{j,l-1}^{(t)^{2}}-2d_{j,l}^{(t)}d_{j,l-1}^{(t)}\cos{(q_{j}^{(0)}-q_{f,j})}},$
(5)

with a similar formulation for the extendable cord length $L_{f,j}^{(e)}$,
noting that (see Fig. 3(b)) the perpendicular offset of the routing point of
the elastic cord is identical for both links at $d_{j}^{(e)}$, and there is
zero initial angular offset:

$L_{f,j}^{(e)}=d_{j}^{(e)}\sqrt{2(1-\cos{(q_{f,j})})}.$ (6)

The perpendicular distance of the tendon to the joint, $L_{f,j}^{(t,\perp)}$
can be found using the angle $\alpha_{j,l}^{(t)}$ that $d_{j,l}^{(t)}$ makes
with the tendon, $L_{f,j}^{(t,\perp)}=d_{j,l}^{(t)}\sin(\alpha_{j,l}^{(t)})$,
where $\sin(\alpha_{j,l}^{(t)})$ can be found using the sine rule:

$\sin(\alpha_{j,l}^{(t)})=\frac{d_{j,l}^{(t)}}{L_{f,j}^{(t)}}\sin(q_{j}^{(0)}-q_{f,j}).$
(7)

This results in a perpendicular tendon distance of

$L_{f,j}^{(t,\perp)}=\frac{d_{j,l}^{(t)^{2}}}{L_{f,j}^{(t)}}\sin(q_{j}^{(0)}-q_{f,j}),$
(8)

and a similar result can be found for the perpendicular distance of the
elastic cord $L_{f,j}^{(e,\perp)}$:

$L_{f,j}^{(e,\perp)}=\frac{d_{j,l}^{(e)^{2}}}{L_{f,j}^{(e)}}\sin(q_{f,j}).$
(9)

When the tendon is stationary, the fingers will move to the position of
minimum energy. Neglecting gravitational potential energy, the elastic
potential energy stored in the elastic extension cord mounted on the dorsal
side of the finger is the driving factor in the stable joint-space
configurations of the finger. The change in elastic potential energy from the
initial position of the finger ($L_{f,1}^{(e)}=L_{f,2}^{(e)}=0$) can be
computed given spring constant $k_{f}^{(e)}$:

$E_{f}^{(e)}=\frac{1}{2}k_{f}^{(e)}(L_{f,1}^{(e)}+L_{f,2}^{(e)})^{2}.$ (10)

Fig. 5 shows the joint space of a finger of the Hydra Hand, with lines of
constant tendon length shown, coloured according to normalised elastic
potential energy. As seen, the minimum-energy joint-space trajectory of the
finger is such that $q_{f,1}$ is minimised until $q_{f,2}$ reaches its joint
limit—this is useful for caging objects—however, also seen is another region
of minimum-energy is when $q_{f,2}$ is minimised, indicating that bistable
grasping behaviour could be achieved with small design alterations. Bistable
mechanisms have been used in literature to produce secure, low actuation
energy grasps [23, 24], and there is scope for future work to explore this for
the Hydra Hand. Many underactuated grippers balance the extension springs such
that the proximal joints are energetically favourable to actuate, meaning a
precision/parallel grasp is the default behaviour of the gripper, and the
distal joints do not flex until the proximal phalanges are physically blocked
by a grasped object [19]. This form of compliance is favourable when grasping
rigid objects, but may not be suitable for delicate or articulated objects,
where caging the object via form closure may be a safer solution.

###  II-C Hydraulic Actuation and Adaptability

Figure 6: Tendon actuation via McKibben muscles. (a) Relaxed state. (b)
Contracted state. When the motion of one tendon is blocked, pressure is
distributed to the other actuating muscle. This allows independent tendon
motion.

During operation in power grasping mode the fingers of the gripper should move
with a degree of independence in order to accommodate for irregular objects of
unknown size and shape. This is difficult with a single actuator, with
existing hands utilising pulley balancing systems [25, 16, 19, 26, 27] and
other differentials [28, 29, 30] to achieve this. A simple alternative is to
use hydraulic actuation, whereby pressure can be equally shared across the
fingers; if one finger makes contact with an object, pressure continues to
actuate the others. This may be preferable in some scenarios over a tendon-
based differential, where friction builds exponentially with tendon length in
the absence of suitable bearings. However, with a tendon-driven hand achieving
this requires a method of converting pressure to tendon motion. To achieve
this, we utilise McKibben muscles [31] to convert fluid pressure to linear
motion. Shown in Fig. 6, two muscles are used — one for each pair of fingers.
When the gripper is in precision mode, one finger from each pair makes up the
combined finger, ensuring that the two combined fingers flex as one fully
actuated gripper. This is beneficial when grasping small flat objects; if the
object is laterally offset then the closest finger will push the object
towards the centreline of the gripper, and the opposing finger will not swing
through prematurely. In power grasping mode, it is possible that irregularly
shaped objects can block the motion of a finger pair, leaving one finger
without contact. However, given that the object is graspable, it will move
towards the centreline of the gripper until a rigid grasp with a minimum of
three points of contact have been achieved. Water was chosen as a hydraulic
fluid due to its low viscosity and incompressibility under normal conditions,
effectively converting muscle volume change directly to tendon contraction.

##  III Performance Evaluation

The Hydra Hand was evaluated on the YCB Gripper Assessment Benchmark [32] in
each of its three grasping configurations: precision pinch grasp, cylindrical
power grasp, and spherical power grasp. The benchmark consists of $28$ objects
grasped a total of $136$ times - each rigid object is grasped in a series of
positions selected from the following: $\mathcal{O}$ (aligned with the
gripper), $\Delta x$, $\Delta y$, and $\Delta z$ (offset by $1$ cm in the $x$,
$y$, and $z$ directions, respectively). This was repeated for each grasping
mode, giving a total of $408$ attempted grasping actions. The objects included
in the benchmark can be seen in Fig. 7 (a).

To quantify the Hydra Hand’s ability to grasp deformable objects, three of the
Soft Object Gripper Benchmarks [33] were performed: ‘A) flat edge grasp
success and drag placement accuracy’, ‘C) crumpled object encapsulation’, and
‘D) flat non-boundary grasp success’. These were chosen to capture both
precise and power grasping ability. Benchmark ‘B) edge grasp resilience’ was
not performed, as it is a measure of grasping force, rather than the ability
to grasp. The items of clothing used in this benchmark are shown in Fig. 7
(b), and $330$ attempted grasps were performed in total.

Figure 7: Objects used for the YCB Gripper Assessment Benchmark (a) and Soft
Object Gripper Benchmarks (b). Figure 8: Results of the YCB Gripper Assessment
Benchmark. For rigid objects, in each $2\times 2$ square, cells are coloured
dark or light according to respective success or failure according to the
following criteria: top left if a successful grasp is achieved in the vertical
orientation, top right if the object is static for $3$ seconds after being
grasped from above, bottom left if the object remains held while rotated to
horizontal, and bottom right if the object is completely static for $3$
seconds after being rotated. This was repeated at an origin point
$\mathcal{O}$, and at $1$ cm offsets $\Delta x$, $\Delta y$, and $\Delta z$.
For articulated objects, there are $20$ cells showing attempted grasp success
or failure, coloured dark or light accordingly. Each cell is divided according
to grasp mode used: precision, cylindrical, and spherical modes are shown in
orange, magenta, and blue, respectively.

##  IV Results & Discussion

The results of the YCB Gripper Assessment Benchmark are shown in Fig. 8. As
expected, the two compliant power grasping modes fail to grasp small and flat
objects, while the precision grasping mode fails to grasp irregularly shaped
tool objects. Individually, the precise, cylindrical, and spherical grasping
modes achieve scores of $175/404$, $132.5/404$, and $140/404$, respectively.
Together, the combined score of the Hydra Hand is $257.5/404$ (where the
result for each object is taken as the best from the three grasping mode
results) — comparable to the Yale Model B gripper [34], also utilising a
single grasping actuator. Fig. 9, Fig. 10, and Table I show the results of the
Soft Object Gripper Benchmarks A, C, and D, respectively. Benchmark A, where
the gripper must grasp cloth edges and drag the item of clothing $500$ mm
horizontally, and Benchmark D, where each garment was grasped from a central
featureless region, were completed using the precision grasping mode.
Benchmark C, where crumpled items of clothing are grasped then raised until
completely lifted from the table surface, was completed using the spherical
power grasping mode.

Figure 9: Results of the Soft Object Gripper Benchmark A. Items of clothing
are grasped and dragged $500$ mm horizontally, and placement error is
measured. Results for single, double, and folded edges are shown. Bars show
median result, $\circ$ and $\times$ marks show successful and failed attempts,
respectfully.

The precision mode was successful in grasping the majority of small and flat
objects in the YCB Gripper Assessment Benchmark (see Fig. 8) and was able to
grasp all single, double, and folded edges in the Soft Object Gripper
Benchmark A (see Fig. 9), as well as successfully grasping nearly all clothes
at a central featureless region in Soft Object Gripper Benchmark D (see Table.
I). However, some failure cases were observed. The first case was when the
object was too small in height, such as the smallest three washers of the YCB
Gripper Benchmark. The fingertips of the gripper have a ‘fingernail’-style
feature, which is a rigid lip designed to guide objects upwards into the hand
by a small amount, however this fingernail has a finite thickness and it was
not possible to exploit this feature when grasping small, flat objects. The
second case was when grasping heavier objects and stiffer materials. Because
only the proximal joint is active during precision mode, the achievable
grasping force of the gripper is reduced compared to the compliant modes, and
objects located at the distally will require more actuation force. For
example, grasping the largest washer at any offset, grasping central regions
of the denim jeans, and dragging the grasped edges of the hoodie and jeans
were not possible with the Hydra Hand. In practice, grasping strategies to
bring the objects closer to the palm can be used to negate this. In precision
mode, adaptive grasping between pairs of fingers is removed to produce a
repeatable central grasp. This was especially important when grasping edges of
clothing, where it is difficult to predict when one finger will contact the
garment. Had adaptive grasping been utilised, premature contact would have
allowed the non-contacting finger to move past the target grasping point. Due
to the mechanical tolerances required for the raised locking features to
easily connect and disconnect during locking and unlocking, a slight amount of
flexion of the distal finger joints was observed in precision mode. This
slight flexion was negligible and did not impair precision grasping.

Figure 10: Results of the Soft Object Gripper Benchmark C: items of clothing are crumpled, then grasped and lifted until contact with the table is completely broken. Lift height required to break contact with the table (of successful grasps) is shown; Bars show median result, $\circ$ and $\times$ marks show lifts that did and did not break contact with the table, respectively. Note that the Hydra Hand failed to grasp the jeans $3/5$ times in the crumpled state. Socks |  Knickers |  Boxers |  Bra |  Scarf |  T-Shirt |  Vest |  Dress-Shirt |  Skirt |  Nightdress |  Jeans |  Hoodie |  Fleece
---|---|---|---|---|---|---|---|---|---|---|---|---
1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.8 | 1.0 | 0.8 | 1.0 | 1.0 | 0.0 | 1.0 | 1.0
Table I: Results of the Soft Object Gripper Benchmark D. Items of clothing are
grasped at a flat, non-boundary region, and grasp success rate is recorded.

The cylindrical mode was only used in the YCB Gripper Assessment Benchmark
(see Fig. 8), and excelled at grasping irregularly shaped tool objects (the
clamps), and mid-sized spherical objects. Some failures in this mode were
observed when a large force was exerted along the axis of the cylindrical
grip, for example when the drill was rotated. A further failure mode occurred
when the fingers reached their grasping point at a higher position above the
grasping surface, limiting the grasps of objects with smaller heights such as
the pen, scissors, and screwdriver. This occurs because the distal joint of
the fingers flex first, and the fingertips sweep through to meet at a point
above the surface.

Spherical mode was used in both the YCB Gripper Assessment Benchmark (see Fig.
8) and Soft Object Gripper Benchmark C (grasping crumpled clothes, see Fig.
10). This grasping mode, as expected, was successful at grasping round
objects, and was also able to grasp irregularly shaped tools with a similar
proficiency to the cylindrical grasp. Failure occurred during round object
grasping when the objects were smaller than the caging shape of the gripper,
such as the golf ball. Here, the golf ball was grasped with the fingertips and
lifted successfully, but rolled within the gripper during rotation. This
occurred because the fingertips have relatively low friction, and the
fingertip force exerted on the object has a component that acts towards the
palm of the hand — this is balanced when the hand is above the object, but
during rotation the object is effectively pushed into the hand. For larger
objects, spherical grasp enabled greater form closure of the drill than
cylindrical mode, meaning the drill could be rotated stably. In spherical
mode, the height at which the fingertips meet is lower than in cylindrical
mode. This is because the fingers are offset laterally and therefore the plane
in which they flex does not pass through the centre of the gripper. In
spherical mode, the planes intersect earlier into the flexion motion, meaning
that the point at which the fingertips meet is closer to the grasping surface.
This allowed the gripper to pick up the screwdriver successfully, which was
not possible in cylindrical mode. Spherical mode was successful at grasping
and lifting every item of clothing except the jeans in a crumpled state.
Similar to the issue faced when grasping the jeans in precision mode, the
weight and stiffness of the material made it difficult to grasp the jeans
unless a graspable feature was already present; it was difficult to deform the
jeans with the gripper to create graspable features. In the compressed
crumpled state, there are a higher density of graspable features on the item
of clothing, enabling grasping.

Finally, the challenging manipulation task of grasping a bunch of grapes then
picking a single grape was performed as a pilot task to demonstrate the
versatility of the Hydra Hand. Shown in Fig. 11, the bunch of grapes was caged
using spherical grasping mode, then placed at a secondary location. Once
placed, a single grape was picked from the bunch using the precision grasping
mode with a hard coded twist-and-pull action performed by the robot arm. To
grasp a delicate, articulate object such as a bunch of grapes is challenging
for many grippers, and is only possible because of the energetically
favourable flexion motion of the distal joints of the fingers, which allows
the gripper to cage objects without exerting excess forces on them. Picking a
single grape from the bunch is also challenging, requiring precise motion of
the fingers of the gripper to not disturb other grapes in the bunch and to
hold the grape firmly enough to pick it, but not so hard that it crushes the
grape.

##  V Conclusions & Future Work

Figure 11: A demonstration of the Hydra Hand on a challenging manipulation
task. (a) Using spherical mode to grasp a bunch of grapes via caging. (b)
Using precision mode to pick a single grape from the bunch.

In this study we have presented the Hydra Hand, a gripper that is capable of
switching between four-fingered underactuated compliant spherical and
cylindrical grasping modes and a two-fingered rigid precision grasping mode.
With only one grasping actuator, compliance and adaptability between pairs of
fingers were achieved via underactuation and pressure distribution through
McKibben muscles. Experimental results from the YCB Gripper Assessment and
Soft Object Gripper Benchmarks demonstrate the ability of the gripper to
perform its target grasps on rigid and deformable objects of varying size,
shape, and weights. Results highlight a range of grasping applications from
small objects and precise edge grasping to large objects and articulated
object caging.

Future work will investigate actively using the rotating palm for in-hand
manipulation, and exploring how the mode-switching behaviour can be used in
planning grasping strategies of unknown objects. Generating large grasping
forces in precision mode was difficult due to the physical locking of the
distal joints leaving only the proximal joints to generate force. Further
development is needed to address this.

## Acknowledgements

The authors thank Dongmyoung Lee, Barry Mulvey, and Peter Tisnikar for their
meaningful feedback on this work.

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