# Gaka-chu: a self-employed autonomous robot artist

Eduardo Castelló Ferrer1,2, Ivan Berman3, Aleksandr Kapitonov3,
Vadim Manaenko3, Makar Chernyaev3, Pavel Tarasov3
1MIT Media Lab, Massachusetts Institute of Technology, Cambridge, USA
2MIT Connection Science, Massachusetts Institute of Technology, Cambridge, USA
3M2M Economy, Inc (“Merklebot”), San Francisco, CA, USA

###### Abstract

The physical autonomy of robots is well understood both theoretically and
practically. By contrast, there is almost no research exploring their
potential economic autonomy. In this paper, we present the first economically
autonomous robot—a robot able to produce marketable goods while having full
control over the use of its generated income. Gaka-chu (“painter” in Japanese)
is a 6-axis robot arm that creates paintings of Japanese characters from an
autoselected keyword. By using a blockchain-based smart contract, Gaka-chu can
autonomously list a painting it made for sale in an online auction. In this
transaction, the robot interacts with the human bidders as a peer not as a
tool. Using the blockchain-based smart contract, Gaka-chu can then use its
income from selling paintings to replenish its resources by autonomously
ordering materials from an online art shop. We built the Gaka-chu prototype
with an Ethereum-based smart contract and ran a 6-month long experiment,
during which the robot created and sold four paintings, simultaneously using
its income to purchase supplies and repay initial investors. In this work, we
present the results of the experiments conducted and discuss the implications
of economically autonomous robots.

††Corresponding author: Eduardo Castelló Ferrer<EMAIL_ADDRESS>
##  I Introduction

Robots and cyber-physical systems deployed in the real world are reaching
increasing levels of autonomy and versatility. They can be programmed to
perform tasks with little to no human intervention and can vary significantly
in size, functionality, mobility, dexterity and intelligence. In general,
physical autonomy (i.e., the ability to observe and act on a physical
environment autonomously) is well understood and the state of the art is quite
advanced, both theoretically and practically. However, the increasing physical
autonomy of devices and systems in the real world opens up new issues beyond
the physical environment: for instance, security, accountability,
auditability, and other social and ethical issues [1, 2, 3]. In contrast to
physical autonomy, very little research has been done on the social and
economic autonomy (i.e., the ability to observe and act on a social or
economic environment independently) of robots and cyber-physical systems.

The idea of artificial economic autonomy has been explored in philosophical,
social, legal, and economic theory. For instance, a line of research
considering software-based AI agents has explored the concept of machina
economicus [4], or rational AI agents that can reason in economic contexts,
either as an ideal synthetic version of the perfectly rational homo economicus
[5], or using new incentive mechanisms. Early practical examples of this
concept include autonomous software agents for automatic trading [6] or AI-
driven setting of market prices [7], in which AI agents might perform better
economic reasoning than humans. The concept of machina economicus has also
been expanded from artificial agents that mimic humans to the concept of
automata economicus, in which artificial agents achieve new types of economic
value creation and build an artificial “creative economy” [8]. A few existing
works have also looked at economic autonomy specifically in robots. For
instance, [9] defines a fully economically autonomous robot as one that can
use its generated income to cover the costs of its manufacture and
maintenance. In the field of management and marketing, [10] explores the
question of what happens when domestic robots and digital assistants not only
make purchases on behalf of their owners, but take on consumer
responsibilities such as filtering goods and services for their owner to
choose from. The legal implications of economically autonomous robots are also
being explored, e.g., robot-based tax systems [11], copyright and intellectual
property rights of robot creations [12, 13], and whether robots need to obey
copyright law [14]. Although some of these theory developments are more than a
decade old, there have been almost no practical advances with real robots, and
no prototype of an economically autonomous robot has yet been built.

In this paper, we present the first prototype of an economically autonomous
robot, i.e., a robot that can autonomously generate income by producing
marketable goods or services and can use the income to autonomously maintain
itself through the purchase of resources. Our economically autonomous robot
Gaka-chu (“painter” in Japanese) is a self-employed robot artist that
autonomously makes physical paintings, sells them in online auctions, and uses
the income it generates to purchase art supplies from an online shop and repay
start-up loans from initial investors. We built Gaka-chu using blockchain
technology, through rules encoded as Smart Contracts (SCs)—computer code
embedded in the blockchain that directly controls the transfer of digital
assets between parties [15]. The control logic for the robot resides in the SC
while the actuation takes place in the physical world.

###  I-A Blockchain-based robotics

The first proof-of-concept robotics system that used blockchain technology to
make financial transactions was the ADEPT [16] protocol introduced by Samsung
and IBM. The project focuses on increasing the autonomy of devices or machines
that operate in a decentralized manner within (industrial) IoT. For their
proof-of-concept, they used a washing machine (W9000) that could autonomously
order detergent every time it ran out. The ADEPT project also led to a pilot
of a Blockchain-of-Devices (BoD), where devices work together autonomously and
make decisions about tasks or orders [17]. Along these lines, the Plantoid
(2015) art project by Okhaos222News articles on the Plantoid (2015) art
project by Okhaos: in Furtherfield, “Plantoid: The Blockchain-Based Art That
Makes Itself” by Robert Myers, available at
https://www.furtherfield.org/tag/autopoesis, and in Coindesk, “This Robot
Plant Needs You and Bitcoin to Reproduce” by Grace Caffyn, available at
https://www.coindesk.com/this-robot-plant-needs-you-and-bitcoin-to-reproduce.
proposed a metallic robotic sculpture designed to look and move like a plant
and be displayed in a public space. If humans enjoy the sculpture and make a
small donation to it, the sculpture dances with plant-like movements, music,
and lights. Contributions are made via the Bitcoin blockchain. Once the
sculpture has collected a sufficient quantity of Bitcoins in its crypto
wallet, it asks humans to help it by reproducing it (creating a new sculpture)
and placing the “offspring” in a new location.

However, in the first example (ADEPT), the washing machine did not have
control over its income nor the values used to complete tasks or make the
orders, and in the second example (Plantoid), the robotic sculpture did not
produce external goods or services nor did it make consumer choices and
purchases autonomously. Moreover, its donation-based model might also lead to
long wait times and deadlocks. By contrast, an economically autonomous robot
must have control over the action of entering the market to offer goods or
services and over the consumer purchases it makes using the income that it
generates. Therefore, although these two projects are important precedents in
devices making transactions using blockchain-based technology, they are not
economically autonomous robots.

Beyond economic autonomy, recent research has demonstrated many security and
robustness benefits of combining autonomous robots with blockchain-based
technology [18, 19, 20], using both SCs and Merkle trees. In these research
works, the robots are used as nodes in a network and their interactions are
encapsulated in cryptographic transactions. Blockchain technology can give
data confidentiality and entity validation to robots [21, 22], making them
suitable for applications in which privacy and security are a concern [23,
24].

In summary, previous literature has overlooked a prototype of an economically
autonomous robot that not only directly manages its income generation and the
resources it needs to maintain its creation of economic value (e.g.,
recharging batteries, paying for supplies), but also adapts to market needs.
Historically, robots have participated in labor roles (e.g., in factories,
assembly lines), but with new decentralized financial tools such as
blockchain-based SCs, robots can now also take part in other aspects of our
economic environment, redefining their role as not mere tools but potential
peers.

Figure 1: A) Gaka-chu (“painter” in Japanese), a 6-axis robot arm (KUKA KR6
R900) programmed using an Ethereum-based SC while painting a canvas. Gaka-
chu’s work area dimensions are $2.53\times 2.57$ meters and are specially
designed to maximize safety. B) Typical workflow for a painting job: (1) The
robot uses its sensing, computation, and actuation capabilities to paint a
canvas. (2) The robot puts the painting up for auction, participants can place
their bids in the auction website. (3) A winner is selected when the painting
process is finished and the final bid is deposited (by using ETH: the
cryptocurrency of the Ethereum network). (4) Ownership of the painting is
transferred to the winner of the auction. (5) The payment is transferred to
the robot’s account. (6)-(7) The robot can order supplies from an art shop to
maintain its painting activities. Figure 2: Workflow for topic selection:
Gaka-chu first (I) selects the English keyword with the largest number of
searches that day in Google Trends, (II) translates the keyword into Japanese
characters, (III) converts the resulting text into an image, and (IV) uses the
image as an input for the physical painting process.

###  I-B Gaka-chu: a self-employed autonomous robot artist

In this paper, we present an economically autonomous robot named Gaka-chu.
Gaka-chu paints canvases, uses blockchain-based technology to sell the
paintings it makes in online auctions, generates and collects income, and uses
its income to purchase the material resources needed to maintain its activity,
all with minimal human intervention (Fig. 1). Finally, Gaka-chu is able to pay
back initial investors. The three main challenges of an economically
autonomous robot are that: (1) the robot should generate income to maintain
itself, (2) the robot should have a mechanism that adapts to market changes,
and (3) when interacting with third-party agents such as online shops or human
bidders, the robot should do so as a peer, not as a tool. In this paper, Gaka-
chu meets these three main challenges, demonstrated in a 6-month experiment.
Our findings show that Gaka-chu can reach economical autonomy: fulfil a job,
get rewarded for it, and invest the benefits in its own sustainability.

The remainder of this paper is structured as follows. Section II describes
(II-A) how the robot selects a topic for painting, (II-B) the sensing,
planning, and actuation when painting a canvas, (II-C) how the online auctions
are organized and financial transactions made, and (II-D) how the robot
interacts with the autonomous online shop to provide the robot with the
necessary painting consumables. Section III presents the results of a 6-month
experiment including a start-up “loan” from human investors, making and
auctioning four paintings, and fulfilling financial transactions with webshops
and human peers (suppliers, and customers). Section IV discusses the results,
and the implications of economically autonomous robots, and proposes future
directions. Finally, Section V provides our conclusions.

##  II Methods

As any artist painter, Gaka-chu has four main challenges: (1) how to select a
topic for the painting, (2) how to actually paint it, (3) how to sell the
painting to obtain economic resources, and finally, (4) how to use the
generated income to purchase the necessary materials to continue with its
activity. All involved software described in this section is available at our
public GitHub repository333Github repository: https://github.com/Multi-Agent-
io/gaka-chu.online.

###  II-A Selecting a topic for a painting

Each painting by Gaka-chu consists of a set of Japanese characters which
together form a keyword. Gaka-chu adapts to the market by choosing the
keywords that are popular in Google Trends444Google Trends:
https://trends.google.com. The topic selection process (Fig. 2) proceeds as
follows: (I) Gaka-chu requests the keyword with the maximum number of searches
for the current day from the Google Trends API. (II) Gaka-chu translates the
selected keyword to Japanese using the Google Translate555Google Translate:
https://translate.google.com API. (III) Gaka-chu converts the text-based
Japanese characters into an image of black strokes (forming the Japanese
words) in the center of a white background, using the Python Image Library
(PIL) [25], and (IV) saves the image for further processing. In this approach,
Gaka-chu chooses topics that are generally popular and potentially profitable
for sale, and still the paintings are not associated with any previous
copyright claims.

###  II-B Painting process

After an image of the selected keyword is created and stored, Gaka-chu starts
the physical painting process. The three main components—sensing, planning,
and actuation—are depicted in Fig. 3.

Figure 3: Functional scheme and workflow for the Gaka-chu painting process. In
summary, the sensing part gets the information about the canvas (e.g.,
position, orientation) from a depth camera installed at the end-effector. This
information is sent to the planning part, which runs in an Intel NUC single-
board computer and calculates all trajectories for the Gaka-chu joints.
Finally, in the actuation part, calculated trajectories are sent to the
internal motor controllers, which execute the movements.

####  II-B1 Sensing

Figure 4: An example of canvas detection using the Intel RealSense D435i depth
camera. The image shows the angle of rotation of the canvas relative to Gaka-
chu’s horizontal axis.

In the sensing pipeline (see the left block of Fig. 3), a depth camera (Intel
RealSense D435i) is mounted on Gaka-chu’s end-effector and connected to a
single-board computer (Intel NUC7i5BNB) via USB. The position and orientation
of the canvas is detected from the point cloud information from the depth
camera (Fig. 4). This process is handled by a ROS [26] camera node, working in
client-service mode and based on the realsense
library666https://dev.intelrealsense.com. The ROS camera node also publishes
the dimensions and center point of the canvas which are necessary for the
correct coordinate transformation from the canvas to the camera frames. It
then provides this information upon request to the planning component of the
system through its ROS interface.

####  II-B2 Planning

Figure 5: An example of the skeletonization and painting process. (A) 5
characters form the Japanese term 女性史月間 obtained after translating the Google
Trends keyword “Women’s History Month.” (B) The same image after
skeletonization of the 2D shapes into 1D curves and conversion from pixels to
meters. Different colors represent different strokes in the painting process.
(C) An image of the finished painting.

In the planning pipeline (see the center block of Fig. 3), Gaka-chu calculates
the end-effector trajectories needed to paint the image generated in Sec.
II-A. First, Gaka-chu pre-processes the stored image by converting the binary
2D shapes of the Japanese characters into 1D curves, i.e., “skeletonization”
[27] (Fig. 5 A and B), using the OpenCV library [28]. The 1D curves are
represented as pixels in a 2D coordinate frame. The pixel coordinates are
passed to the Local Task Planner (LTP), which translates the pixels of the
skeletonized image from their starting image-based coordinate frame to Gaka-
chu’s canvas coordinate frame, with the $z$ axis taking into account the 3D
position of the canvas relative to Gaka-chu. Then, the LTP coverts the
coordinates from pixels to meters. At this point, Gaka-chu has all the
necessary coordinates to paint the strokes that will form the desired
painting.

For Gaka-chu’s motion planning, we use the MoveIt Framework [29], with a
customized MoveIt module for inverse kinematics. Our custom module extends the
standard module by taking into account that the manipulator in different
positions has different kinetic energy (i.e., the moment of inertia changes).
This would eventually lead to blockage of the movement by the KUKA internal
controller. First, the planner interpolates paths to build splines relative to
the end-effector coordinate system. Our MoveIt module solves the inverse
kinematics and obtains paths for each link of the robot. The motion planner
then conducts time parameterization, taking into account the maximum speeds
and accelerations for each link of the robot, and finally prepares the
calculated trajectories to be sent from ROS to the Ethernet KRL Interface
(EKI)777EKI is a TCP/IP protocol which allows the exchange of XML and binary
data between the manipulator and the external system..

####  II-B3 Actuation

In the actuation pipeline (see the right block of Fig. 3), Gaka-chu’s internal
computer runs executable code written in the KUKA Robot Language (KRL)—a
programming language similar to Pascal. The KRL program receives the
trajectory information calculated in the planning pipeline through the EKI
interface and organizes the transfer of the necessary rotation angles, speeds,
and accelerations to the KUKA drives. If the controller detects any problems
during the movement (e.g., physical impossibility of movement), the
manipulator stops and the corresponding error messages are sent back.

The technique Gaka-chu uses to paint the canvas is based on the existing
literature on drawing with strokes [30, 31]. The paint cup is installed at a
known position, to one side of the manipulator. The painting algorithm is
organized as follows: (1) the robot moves to the position above the paint cup,
(2) it dips the brush into the paint and withdraws it, (3) it moves to the
starting point of the trajectory with a slight offset along the $z$ axis, (4)
it descends to the starting point, (5) it paints one segment of the
trajectory, and (6) it raises the brush and repeats the process until the
whole painting is complete (Fig. 5 C). After testing several types of paints
and brushes, we realized that nylon and bristly brushes as well as acrylic
paint show the best finishing quality.

Figure 6: Screenshot of the auction site where Gaka-chu advertises the
completed paintings. Potential customers bid with their own blockchain-based
assets until the auction closes and the highest bidder wins the auction lot.
Four completed auctions, illustrated in the balance graph (Fig. 7), are
located at the very end of the list.

###  II-C Generating income: selling paintings

After the painting has been completed, Gaka-chu puts it up for sale in an
online auction (Fig. 6). The online auctions are hosted on an external
service, where Gaka-chu maintains its own page888Rarible auction page:
https://rarible.com/gakachu. The logic of the auction is encoded in an
Ethereum-based SC999https://docs.rarible.org/ethereum/smart-contracts/smart-
contracts/ and is uploaded in the Ethereum Mainnet blockchain.

When a painting is completed, Gaka-chu advertises its availability by creating
a dedicated tab for it within its own page and uploading a video of the entire
painting process. Human bidders are then able to place bids in a given time
limit in ETH (i.e., the cryptocurrency of the Ethereum blockchain) using their
own crypto wallets. Once the auction is closed, the ownership of the painting
is assigned101010Digital ownership is assigned using a non-fungible token
(NFT). More information is available at www.gaka-chu.online. to the Ethereum
address of the highest bidder, and the bid amount is sent to Gaka-chu’s
Ethereum address. The process of arranging shipping and delivery of the
physical painting to the mailing address of the highest bidder is of course
arranged off-chain, with the manual help of a human assistant.

###  II-D Using income: purchasing consumables and paying back investors

Gaka-chu can use its generated income to order the art materials needed for
future painting jobs (e.g., canvases, paints, brushes). Gaka-chu autonomously
places orders at an online art shop that accepts payments in ETH by using a
web-app we developed111111Art shop interface:
https://old.dapp.robonomics.network/#/art-shop. Gaka-chu decrements its canvas
stock counter each time it completes a painting, and decides to purchase a set
of supplies (canvases, paints, and brushes) when the counter reaches one.
Using this software, Gaka-chu generates a message containing the composition
of the order, the Ethereum address, and the amount of ETH tokens to be paid,
and sends the message to the art shop through its server API. If the
parameters for the purchase are acceptable, the art shop agrees to execute it.
All steps in this two-way communication are conducted through an Ethereum-
based SC, obliging the art shop to fulfill its liabilities to provide
consumables. The SC ends with the sending of a final message about a
successful purchase and the sending of ETH tokens to the shop’s Ethereum
wallet. When supplies are received by post, a human assistant places the
canvas, paint, and brush in their positions. Finally, after successfully
selling a batch of paintings, Gaka-chu pays back the initial investments that
were used to start its activity (e.g., pay for the auction site platform and
sign-up fees).

##  III Results

To demonstrate the feasibility of an economically autonomous robot, we ran a
6-month experiment in which Gaka-chu received a starting “loan” from early
human investors, painted canvases and sold them at auction to human bidders,
used the generated income to maintain its economic activity by purchasing
supplies, and paid back its human investors. During the experiment, Gaka-chu
painted four canvases which were put up and sold at online auctions.

Figure 7: Balance on Gaka-chu’s Ethereum wallet from March 22nd 2021 to
September 3rd 2021\. The graph is organized as a progressive line chart where
the different stages of the experiment are depicted: (1) funding, (2)
auctioning and (3) investment repayment and supplies acquisition. In the
chart, red squares represent transactions from initial investors to fund the
system. Green diamonds represent the sale of a painting. Blue triangles
represent the purchase of consumables and grey dots represent the payment of
Ethereum network service fees. Yellow dots represent platform service fees for
the auction site. Finally, orange squares represent loan repayments to initial
investors.

Figure 7 shows the balance changes on Gaka-chu’s Ethereum wallet121212Gaka-
chu’s Ethereum wallet address:
https://etherscan.io/address/0xcc3672c869c923b90f2c1bfba2c7801e3924114a during
a 6-month period of time (March to September 2021). Figure 7 is organized as a
line chart where the different stages of the experiment are depicted: (1)
funding, (2) auctioning and (3) investment repayment and supplies acquisition.
Initially, in order to setup an account on the auction site, Gaka-chu requires
to send several transactions and pay network fees (grey dots). For that
purpose, Gaka-chu received an initial “loan” from human investors (red squares
transactions). Gaka-chu then sold four completed paintings at different
auctions, transferred the ownership of the paintings to the highest bidders,
and in return received the corresponding bid amount in ETH (green diamonds).
Gaka-chu was then able to pay back its human investors (orange square),
purchase more art supplies (blue triangles) and pay the auction site fees
(yellow dots). The videos of all Gaka-chu’s completed paintings and results of
its four closed auctions can be retrieved from the auction host website.

##  IV Discussion

Robots of all types, from autonomous vehicles to 3D printing devices, are
revolutionizing a wide variety of industries: mobility, manufacturing, and
logistics. The emergence of robotics is commonly acknowledged as one of the
main disruptive changes that will have substantial socioeconomic impact in
upcoming decades [32]. As countries and companies adjust to increasing social
and economic shifts, interest in robotics will continue to increase—automation
is key to improving productivity. In the one hand, financial software-based
agents (e.g., Paypal, High Frequency Trading, eCommerce) have had a huge
impact on productivity, but they do not impact the physical realm directly. On
the other hand, modern robots and cyber-physical systems interact with the
physical world in powerful ways (i.e., sense, plan, and actuate), but they
lack the technical ability to directly participate in financial transactions
due to insufficient agency.

However, blockchain-based technologies such as SCs allow robots to become
online, digital economic actors that can financially operate with minimal
human intervention. The combination of both fields opens the door towards
financial autonomous robots that can redefine their role as not mere tools but
potential peers. This opens a debate not only about the concept of property
and ownership under increasing robot autonomy [33] but also whether these
systems can become a future cornerstone of societal economic activity (e.g., a
Universal Basic Income [34] through taxation of robot’s economical
activities).

For future work, we would like to focus on improving the selection of the
keyword to paint. We consider that after a series of sales, the topic
selection strategy could be adjusted based on historical data. In addition, we
would like Gaka-chu to move beyond calligraphy paintings and let it explore
other artistic styles (e.g., abstract art, caricature portraits, and
pointillism paintings) and modes of production. Finally, financially
autonomous robots also need to be able to make purchases to maintain their
continued viability. For instance, future robotics systems using this
technology might need to be able to pay for electricity (e.g., to a street
charger), spare or replacement parts (e.g., to Mouser), training data (e.g.,
to MTurk), or skilled human resources (e.g., to TaskRabbit) with their
blockchain-based financial assets. Developing new payment gateways and
interfaces to these services is a promising way forward.

##  V Conclusions

In the robotics research field physical autonomy (i.e., the ability to observe
and act on a physical environments) is well understood and the state of the
art is advanced. However, very little research has been done on the economic
autonomy of robots. In this work, we have presented and demonstrated the first
economically autonomous robot, using blockchain-based smart contracts to build
Gaka-chu, a self-employed autonomous robot artist. In the proposed system, the
control logic for the robot resides in the smart contract uploaded to the
Ethereum blockchain while the actuation takes place in the physical world. In
this research, Gaka-chu makes physical paintings, sells them to humans in
online auctions, and uses the income it generates to purchase art supplies
from an online shop, and pay back initial investors. Our findings show that
after a 6-month long experiment including a start-up “loan”, making and
auctioning four paintings, and making financial transactions with webshops and
human peers (suppliers, and customers), Gaka-chu can reach economic autonomy:
fulfil a job, get rewarded for it, and invest the benefits in its own
sustainability.

## References

  * [1] S. Bijani and D. Robertson, “A review of attacks and security approaches in open multi-agent systems,” _Artificial Intelligence Review_ , vol. 42, no. 4, pp. 607–636, 2014.
  * [2] R. N. Akram, K. Markantonakis, K. Mayes, O. Habachi, D. Sauveron, A. Steyven, and S. Chaumette, “Security, Privacy and Safety Evaluation of Dynamic and Static Fleets of Drones,” 2017.
  * [3] “The moral machine experiment,” _Nature_ , vol. 563, no. 7729, pp. 59–64, 2018. [Online]. Available: https://doi.org/10.1038/s41586-018-0637-6
  * [4] D. C. Parkes and M. P. Wellman, “Economic reasoning and artificial intelligence,” _Science_ , vol. 349, no. 6245, pp. 267–272, 2015.
  * [5] S. D. Levitt and J. A. List, “Homo economicus evolves,” _Science_ , vol. 319, no. 5865, pp. 909–910, 2008.
  * [6] D. Minarsch, S. A. Hosseini, M. Favorito, and J. Ward, “Autonomous economic agents as a second layer technology for blockchains: Framework introduction and use-case demonstration,” in _2020 Crypto Valley Conference on Blockchain Technology (CVCBT)_ , 2020, pp. 27–35.
  * [7] P. Danassis, A. Filos-Ratsikas, and B. Faltings, “Achieving diverse objectives with ai-driven prices in deep reinforcement learning multi-agent markets,” _arXiv preprint arXiv:2106.06060_ , 2021.
  * [8] G. F. Nobre, “Automata economicus: Machinesthat create value and the artificial creative economy,” _Economía Creativa_ , no. 9, pp. 10–26, 2018.
  * [9] C. Reynolds, A. Cassinelli, and M. Ishikawa, “Economically autonomous robotic entities,” in _International Conference on Robotics and Automation, ICRA_ , vol. 7, 2008.
  * [10] S. H. Ivanov and C. Webster, “The robot as a consumer: a research agenda,” in _Marketing: experience and perspectives” Conference_ , 2017, pp. 29–30.
  * [11] S. H. Ivanov, “Robonomics-principles, benefits, challenges, solutions,” 2017.
  * [12] C. R. Davies, “An evolutionary step in intellectual property rights–artificial intelligence and intellectual property,” _Computer Law & Security Review_, vol. 27, no. 6, pp. 601–619, 2011.
  * [13] M. Buning, “Is the eu exposed on the copyright of robot creations,” _Robotics Law Journal_ , vol. 1, no. 3, pp. 8–9, 2015.
  * [14] J. Grimmelmann, “Copyright for literate robots,” _Iowa L. Rev._ , vol. 101, p. 657, 2015.
  * [15] V. Buterin _et al._ , “A next-generation smart contract and decentralized application platform,” _white paper_ , vol. 3, p. 37, 2014.
  * [16] P. Veena, S. Panikkar, S. Nair, and P. Brody, “Empowering the edge-practical insights on a decentralized internet of things,” _Empowering the Edge-Practical Insights on a Decentralized Internet of Things. IBM Institute for Business Value_ , vol. 17, 2015.
  * [17] S. Higgins, “Ibm reveals proof of concept for blockchain-powered internet of things,” March 2015, [Online; posted 16-March-2015]. [Online]. Available: https://www.coindesk.com/ibm-reveals-proof-concept-blockchain-powered-internet-things
  * [18] V. Strobel, E. Castelló Ferrer, and M. Dorigo, “Managing byzantine robots via blockchain technology in a swarm robotics collective decision making scenario,” in _Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems_ , ser. AAMAS ’18. Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems, 2018, pp. 541–549. [Online]. Available: http://dl.acm.org/citation.cfm?id=3237383.3237464
  * [19] V. Strobel, E. Castelló Ferrer, and M. Dorigo, “Blockchain technology secures robot swarms: A comparison of consensus protocols and their resilience to byzantine robots,” _Frontiers in Robotics and AI_ , vol. 7, 2020. [Online]. Available: https://www.frontiersin.org/article/10.3389/frobt.2020.00054
  * [20] E. C. Ferrer, E. Jiménez, J. L. Lopez-Presa, and J. Martín-Rueda, “Following leaders in byzantine multirobot systems by using blockchain technology,” _IEEE Transactions on Robotics_ , pp. 1–17, 2021.
  * [21] E. C. Ferrer, O. Rudovic, T. Hardjono, and A. Pentland, “Robochain: A secure data-sharing framework for human-robot interaction,” _CoRR_ , vol. abs/1802.04480, 2018. [Online]. Available: http://arxiv.org/abs/1802.04480
  * [22] N. Malsa, V. Vyas, J. Gautam, R. N. Shaw, and A. Ghosh, _Framework and Smart Contract for Blockchain Enabled Certificate Verification System Using Robotics_. Singapore: Springer Singapore, 2021, pp. 125–138. [Online]. Available: https://doi.org/10.1007/978-981-16-0598-7_10
  * [23] E. C. Ferrer, T. Hardjono, A. Pentland, and M. Dorigo, “Secure and secret cooperation in robot swarms,” _Science Robotics_ , vol. 6, no. 56, p. eabf1538, 2021. [Online]. Available: https://www.science.org/doi/abs/10.1126/scirobotics.abf1538
  * [24] R. Müller, A. Kanso, and F. Adler, “An approach to integrate a blockchain-based payment model and independent secure documentation for a robot as a service,” in _Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021_ , T. Schüppstuhl, K. Tracht, and A. Raatz, Eds. Cham: Springer International Publishing, 2022, pp. 177–188.
  * [25] P. Umesh, “Image processing in python,” _CSI Communications_ , vol. 23, 2012\.
  * [26] M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and A. Y. Ng, “ROS: an open-source Robot Operating System,” in _ICRA workshop on open source software_ , vol. 3, 2009, pp. 1–5.
  * [27] P. K. Saha, G. Borgefors, and G. S. de Baja, _Skeletonization: Theory, methods and applications_. Academic Press, 2017.
  * [28] G. Bradski, “The OpenCV Library,” _Dr. Dobb’s Journal of Software Tools_ , 2000.
  * [29] M. Görner, R. Haschke, H. Ritter, and J. Zhang, “Moveit! task constructor for task-level motion planning,” in _2019 International Conference on Robotics and Automation (ICRA)_. IEEE, 2019, pp. 190–196.
  * [30] T. Lindemeier, J. Metzner, L. Pollak, and O. Deussen, “Hardware-based non-photorealistic rendering using a painting robot,” in _Computer graphics forum_ , vol. 34, no. 2. Wiley Online Library, 2015, pp. 311–323.
  * [31] L. Scalera, S. Seriani, A. Gasparetto, and P. Gallina, “Busker robot: A robotic painting system for rendering images into watercolour artworks,” in _IFToMM Symposium on Mechanism Design for Robotics_. Springer, 2018, pp. 1–8.
  * [32] G.-Z. Yang, J. Bellingham, P. E. Dupont, P. Fischer, L. Floridi, R. Full, N. Jacobstein, V. Kumar, M. McNutt, R. Merrifield, B. J. Nelson, B. Scassellati, M. Taddeo, R. Taylor, M. Veloso, Z. L. Wang, and R. Wood, “The grand challenges of science robotics,” _Science Robotics_ , vol. 3, no. 14, 2018.
  * [33] R. Michalski, “How to sue a robot,” _Utah Law Review_ , vol. 2018, no. 5, p. 51, 2018. [Online]. Available: https://ssrn.com/abstract=3757790
  * [34] T. Straubhaar, “On the economics of a universal basic income,” _Intereconomics_ , vol. 52, no. 2, pp. 74–80, 2017.