# Pedipulate: Enabling Manipulation Skills using a Quadruped Robot’s Leg

Philip Arm, Mayank Mittal, Hendrik Kolvenbach, and Marco Hutter All authors
are with ETH Zurich, Robotics Systems Lab; Leonhardstrasse 21, 8092 Zurich,
Switzerland. M. Mittal is also with NVIDIA. Contact<EMAIL_ADDRESS>research
was supported by the Swiss National Science Foundation through the National
Centre of Competence in Digital Fabrication (NCCR dfab). This project has
received funding through ESA contract nos. 4000137333/22/NL/AT and
4000135310/21/NL/PA/pt. This work has been conducted as part of ANYmal
Research, a community to advance legged robotics.

###### Abstract

Legged robots have the potential to become vital in maintenance, home support,
and exploration scenarios. In order to interact with and manipulate their
environments, most legged robots are equipped with a dedicated robot arm,
which means additional mass and mechanical complexity compared to standard
legged robots. In this work, we explore pedipulation - using the legs of a
legged robot for manipulation. By training a reinforcement learning policy
that tracks position targets for one foot, we enable a dedicated pedipulation
controller that is robust to disturbances, has a large workspace through
whole-body behaviors, and can reach far-away targets with gait emergence,
enabling loco-pedipulation. By deploying our controller on a quadrupedal robot
using teleoperation, we demonstrate various real-world tasks such as door
opening, sample collection, and pushing obstacles. We demonstrate load
carrying of more than $2.0\text{\,}\mathrm{kg}$ at the foot. Additionally, the
controller is robust to interaction forces at the foot, disturbances at the
base, and slippery contact surfaces. Videos of the experiments are available
at https://sites.google.com/leggedrobotics.com/pedipulate.

##  I INTRODUCTION

In recent years, the locomotion capabilities of legged robots have greatly
improved. Specifically, quadrupedal robots are nowadays capable of traversing
various industrial and natural environments [1, 2, 3, 4], facilitating their
deployment in inspection, exploration, and search-and-rescue missions [5, 6,
7, 8, 9, 10]. Although legged robots have reached industrial maturity, their
application range currently remains limited to inspection tasks that require
minimal interaction with the environment. We need to improve and robustify
their manipulation capabilities to deploy legged robots in a broader range of
real-world scenarios, such as maintenance, home support, and sample
collection.

Recent works in legged mobile manipulation have mainly focused on systems with
a dedicated robotic arm for interactive tasks such as door opening, fetching
objects, and opening valves [11, 12, 13, 14, 15]. However, such an additional
arm increases the robot’s mechanical complexity and power consumption. Taking
inspiration from quadrupedal animals [16], we hypothesize that many
manipulation tasks do not require the additional dexterity and complexity of
robotic arms and hands but can be solved using the legs of quadrupedal robots.
Using the same limbs for locomotion and manipulation can reduce a robotic
system’s mechanical complexity and cost, which is especially beneficial in
mass-constrained applications such as space exploration.

Figure 1: Our foot target tracking controller enables a variety of real-world
manipulation tasks such as opening doors (A) and fridges (B), object transport
(C), pressing a button (D), pushing obstacles out of the way (E), and
collecting rock samples (F). Figure 2: Overview of our training and deployment
setup. We first specify the commands in an inertial frame, namely the world
frame in training and the control frame during deployment. To make the policy
agnostic to the used inertial frame, we transform the commands to the base
frame before adding them to the observations. The policy’s actions are
interpreted as deviations from the current joint position.

We, therefore, seek to enable a broad range of manipulation tasks using the
legs of quadrupedal robots - so-called pedipulation. By developing a versatile
low-level controller that can track foot target points, we investigate how we
can use pedipulation to enable real-world skills. To this end, we use a deep
reinforcement learning setup to train a robust controller for foot target
point tracking. We additionally seek to enable the controller to approach far-
away targets with the foot in the air while using a tripod gait, facilitating
object transport. We investigate the controller’s tracking performance and
disturbance rejection capabilities in simulation and hardware experiments and
deploy it in various real-world tasks through teleoperation. Specifically, our
key contributions are as follows:

  * •

We design a reinforcement learning-based pedipulation controller by training a
policy that tracks foot position targets.

  * •

We investigate the achievable workspace of our controller while standing and
show that it reaches a large local workspace through inherent whole-body
motions.

  * •

We enable the controller to adapt the robot’s stance and locomote towards far-
away targets via curriculum-based command sampling.

  * •

We demonstrate that the controller is robust to external disturbances, such as
interaction forces on the base and foot, and slippery terrains.

  * •

We show that through the above features, our learned pedipulation controller
enables numerous real-world manipulation skills such as door opening, rock
sample collection, and pushing obstacles without task-specific adaptions (Fig.
1).

##  II RELATED WORK

Several works investigated manipulation using robotic legs - or, more
precisely, pedipulation - in recent years. We split these works into three
categories:

  * •

Manipulation via locomotion: The robot moves objects towards a goal by pushing
them with its body while walking.

  * •

Non-prehensile pedipulation: The robot manipulates objects with its legs
without the capability to exert pulling forces onto the objects.

  * •

Prehensile pedipulation: The robot has a specialized tool on one or multiple
legs to grasp and manipulate objects with pushing and pulling forces.

###  II-A Manipulation via Locomotion

Manipulation via locomotion can enable robots to push objects close to their
body weight [17]. Scaling this strategy to a multi-robot approach allows
manipulating even larger objects [18, 19]. In these works, the authors trained
a reinforcement learning policy to move large-scale objects to a desired 2D
pose. This approach can, for example, be used to remove obstacles. However, it
does not generalize to dexterous tasks such as opening doors or pushing small
buttons, which we aim to tackle in this paper.

###  II-B Non-prehensile Pedipulation

Non-prehensile pedipulation facilitates moving obstacles out of the way [20],
kicking or balancing balls [21, 22], probing the environment [23, 6], or
opening doors [24]. Some of the above approaches track pre-defined
trajectories with a tracking controller. Lu et al. [20] used an inverse
kinematics controller to track a heuristically defined Bézier Curve to push
objects on the ground. Kolvenbach et al. [23, 6] executed a pre-defined
trajectory with FreeGait, an API for whole-body control [25] to haptically
inspect the terrain. Others trained a reinforcement learning policy on a
single task, for example, kicking balls [21] or balancing balls [22]. While
many of these works show promising results in their respective domain, our
goal is to have a general controller that can be used for multiple
manipulation tasks.

Cheng et al. [26] showed a non-prehensile pedipulation controller capable of
generalizing to multiple tasks. In this work, the authors used separately
learned low-level manipulation and locomotion policies and a high-level
behavior tree to synthesize skills such as kicking a ball or pushing buttons.
While the paper shows an impressive range of skills, the low-level
manipulation and locomotion policies are separated, which implies that
combined locomotion and pedipulation maneuvers (loco-pedipulation) are not
feasible. Furthermore, the manipulation policy only tracks half-sine
trajectories near the robot’s nominal foot position. This form of command
sampling probably does not generalize to arbitrary trajectories in the robot’s
workspace. Both these limitations prevent the application of the approach in
more complex scenarios, as considered in this work, like door opening or
object transport.

###  II-C Prehensile Pedipulation

Integrating a gripper on the robot’s foot enables applications such as
fetching objects or collecting samples [27, 28, 29, 30]. Both the Lauron robot
[27, 28] and Mantis [29] are hexapod robots where the integrated grippers can
fetch objects. These works show capable gripper concepts. However, they
provide little detail on their manipulation control approach and do not
demonstrate robustness to external disturbances. Tsvetkov and Ramamoorthy [30]
introduced a dactylus-style gripper for single-leg and two-leg manipulation.
In this work, the gripper concept is remarkable and facilitates manipulating
multiple objects and tools. However, the operator can only command the robot’s
target position in joint space. The target position is converted into a
trajectory and tracked in an open-loop fashion. This approach requires
cumbersome command tuning and limits the robustness to disturbances. In our
work, we aim to overcome these issues and develop a versatile controller which
is useful for both prehensile and non-prehensile pedipulation scenarios.

##  III METHOD

We hypothesize that a hierarchical structure with a task-specific high-level
planner and a generic low-level controller generalizes well to numerous
pedipulation tasks. Here, we focus on the required low-level controller that
effectively tracks foot position commands and is robust to disturbances. Due
to the recent success of reinforcement learning [1, 2, 4, 31, 3], we choose to
use deep reinforcement learning to train a neural network policy (Fig. 2). We
train the policy to track foot target points of the right front foot with
minimal additional rewards. During deployment, the user provides the command
in an inertial frame, and we expect high-level planners to do the same.
However, we want the policy to be agnostic to the used inertial frame.
Therefore, we internally transform the command to the robot’s base frame
before adding it to the observation vector.
We use Isaac Gym as a simulation environment [32, 33] and PPO as a deep
reinforcement learning algorithm [34]. We use MLPs for the actor and critic
networks as in previous work on locomotion [32] and rely on similar
hyperparameters. We randomize simulation friction parameters, add random
pushes to the robot’s base in simulation, and train on irregular terrain to
facilitate the sim-to-real transfer. We deployed our controller on the
quadrupedal robot ANYmal D by ANYbotics [35].

Figure 3: Simulation setup in Isaac Gym. The initial and final sampling space
of the foot position commands are visualized in green and blue, respectively.
The command curriculum enables combined pedipulation and tripod locomotion in
a single policy. The figure shows one robot tracking a close target point
(left) and a robot approaching a far-range target point using the tripod gait
(right). The target points are visualized in yellow. Figure 4: Our controller
enables numerous real-world manipulation tasks: (A) The robot opens a push
door. (B) The robot opens a fridge. (C) The robot lifts a backpack and
transports it to a box using a tripod-hopping gait. (D) The large workspace of
the controller allows pressing a button far above the robot’s base. (E) The
controller can be used to push obstacles out of the way. (F) With an
additional gripper, the robot can collect rock samples.

###  III-A Observation and Action Space

The policy receives proprioceptive information about the robot’s state and the
foot position commands as observations (Table I). The scaled actions are
interpreted as desired deviations from the current joint positions. We tested
two action spaces: deviations from current joint positions and nominal joint
positions. However, we did not notice a relevant difference in the achieved
rewards and behaviors.

Observations |  |
---|---|---
base linear velocity | $\prescript{}{\mathcal{B}}{\bm{v}_{B}}$ | $\mathbb{R}^{3}$
base angular velocity | $\prescript{}{\mathcal{B}}{\bm{\omega}_{B}}$ | $\mathbb{R}^{3}$
projected gravity vector | $\prescript{}{\mathcal{B}}{\bm{g}}$ | $\mathbb{R}^{3}$
joint positions | $\bm{q}_{j}$ | $\mathbb{R}^{12}$
joint velocities | $\dot{\bm{q}_{j}}$ | $\mathbb{R}^{12}$
foot position command | $\prescript{}{\mathcal{B}}{\bm{r}^{*}_{f}}$ | $\mathbb{R}^{3}$
last actions | $\bm{a}_{t-1}$ | $\mathbb{R}^{12}$
Actions |  |
desired deviation from last joint positions | $\Delta\bm{q}_{j}^{*}$ | $\mathbb{R}^{12}$
TABLE I: Observations and actions we use for our foot target tracking policy.
The actor and critic receive the same set of observations.

###  III-B Rewards

We use two types of rewards; a task reward $R_{e}$ representing the main
objective, and penalties $R_{n}$, which render the behavior safe and
physically feasible on the real system. The total reward is $R=R_{e}+R_{n}$.

####  III-B1 Task Reward

We provide a single dense task reward to encourage the foot to be close to the
target point. The reward function is defined as

$\displaystyle R_{e}=w_{1}\cdot
e^{-\left(\frac{||\prescript{}{\mathcal{B}}{\bm{r}}_{f}-\prescript{}{\mathcal{B}}{\bm{r}}_{f}^{*}||}{\sigma}\right)},$
(1)

where $\prescript{}{\mathcal{B}}{\bm{r}}_{f}$ and
$\prescript{}{\mathcal{B}}{\bm{r}}_{f}^{*}$ denote the measured and desired
foot position in the base frame, respectively. We use $w_{1}=15$ and $\sigma$
= $0.8\text{\,}{\mathrm{m}}^{-1}$.

####  III-B2 Normalization Rewards

To reduce jerky motions and enable safe behaviors on the real robot, we add
penalties on quantities such as joint torques, joint velocities, and
collisions between robot links. The normalization reward is defined as

$\displaystyle\begin{split}R_{n}&=w_{2}||\dot{\bm{q}_{j}}||^{2}+w_{3}||\ddot{\bm{q}_{j}}||^{2}+w_{4}||\bm{\tau}||^{2}\\\
+&w_{5}||\bm{a}_{t}-\bm{a}_{t-1}||^{2}+w_{6}\cdot n_{c}+w_{7}\cdot
k,\end{split}$ (2)

where $\dot{\bm{q}_{j}}$ and $\ddot{\bm{q}_{j}}$ denote the joint velocities
and accelerations, $\bm{\tau}$ denotes the joint torques, $\bm{a}_{t}$ the
action at time $t$ and $w_{i}$ the scaling factors. Additionally, we penalize
collisions between shanks and thighs, expressed by the number of collisions at
the current time step $n_{c}$. Lastly, we add a termination penalty, which is
applied to the environments where a base collision is registered, denoted by
$k\in\\{0,1\\}$, where $k=1$ represents a termination at the current time
step. We use the manually tuned reward scales

$\displaystyle w_{2}$ $\displaystyle=-5\cdot 10^{-2}\qquad$ $\displaystyle
w_{3}=-5\cdot 10^{-6}$ $\displaystyle w_{4}$ $\displaystyle=-2\cdot 10^{-5}$
$\displaystyle w_{5}=-1\cdot 10^{-2}$ $\displaystyle w_{6}$ $\displaystyle=-2$
$\displaystyle w_{7}=-80.$

###  III-C Terrain

We observed that training on flat terrain leads to behaviors where stance feet
slide across the ground to adapt the stance. On the real robot, such policies
are prone to stumbling on terrain irregularities. To robustify the behavior,
we train on irregular terrain (Fig. 3). We uniformly sample a height field
between $-0.08\text{\,}\mathrm{m}$ and $0.08\text{\,}\mathrm{m}$ on a grid
with $0.2\text{\,}\mathrm{m}$ resolution to create the terrain.

###  III-D Command Curriculum for Loco-Pedipulation

To enable tracking far-away targets that require the robot to adapt the stance
legs or walk on three legs, we implemented a curriculum for adaptive command
sampling. Initially, commands are sampled uniformly in a box that is reachable
for the foot without moving the stance feet (green box in Fig. 3). Once the
agent learns to track the commands with an average error of less than
$0.06\text{\,}\mathrm{m}$, the command sampling box is extended by
$0.2\text{\,}\mathrm{m}$ in the robot’s heading direction and
$0.2\text{\,}\mathrm{m}$ in both lateral directions. We limit the box to
$2.2\text{\,}\mathrm{m}$ x $3.0\text{\,}\mathrm{m}$ x $1.3\text{\,}\mathrm{m}$
(blue box in Fig. 3) and let the training converge with this command range.
The command range does not limit the range of loco-pedipulation maneuvers
since we can still give a sequence of target points to reach a further target.

###  III-E Sim-to-Real Transfer, Training, and Deployment

We randomize the friction parameters in the simulation and add noise to the
observations. Additionally, we exert random pushes onto the robot’s base every
$3.0\text{\,}\mathrm{s}$, which we simulate by setting a random twist to the
base link. Each twist component is sampled in $[-0.6,0.6]$
$\text{\,}\mathrm{m}\text{\,}{\mathrm{s}}^{-1}$ or $[-0.6,0.6]$
$\text{\,}\mathrm{rad}\text{\,}{\mathrm{s}}^{-1}$. Furthermore, we add a
disturbance force at the feet of up to $12\text{\,}\mathrm{N}$ that is
constant over an episode. Finally, we use an actuator network to simulate the
actuator dynamics as in [36]. We train with an episode length of
$12\text{\,}\mathrm{s}$ and resample commands every $6\text{\,}\mathrm{s}$.
Training takes $5.5\text{\,}\mathrm{h}$ on an NVIDIA GeForce RTX 2080 Ti for
8000 iterations.

During deployment, we define the command in a fixed local control frame
($\prescript{}{\mathcal{C}}{\bm{r}_{F}^{*}}$). When our pedipulation
controller starts, it locks the control frame at the current base position,
and our SLAM solution [37] continuously updates the transforms between the
map, the control, and the base frame. We define the command by the joystick or
the GUI in the control frame and transform it into the base frame
($\prescript{}{\mathcal{B}}{\bm{r}_{F}^{*}}$) before adding it to the policy
observations. The control frame allows the user to intuitively control the
foot in a fixed frame rather than having the behavior influenced by the
current base pose. The user can use the joystick to incrementally move the
target point or a GUI interface to directly command a single far-away target
point (Fig. 2).The policy and the PID controller run at
$50\text{\,}\mathrm{H}\mathrm{z}$ and $400\text{\,}\mathrm{H}\mathrm{z}$,
respectively.

##  IV RESULTS AND DISCUSSION

###  IV-A Real-World Manipulation Skills

Tracking foot target points in a large workspace via whole-body motions allows
for solving various manipulation tasks (Fig. 4). This section presents
multiple manipulation skills we achieved on the real system using our designed
pedipulation controller and teleoperation interface. The operator had a direct
line of sight to the robot in all experiments.

####  IV-A1 Door Opening

Door opening requires a controller that is robust to interaction forces with
the environment. Additionally, reaching the door handle with the robot’s foot
requires a large workspace. Lastly, passing the door when it is open requires
a combined loco-pedipulation behavior.
The robot was able to open a push door with a resistive spring-damper element
at the hinge (Fig. 4A). We used the joystick interface to push down the handle
and then set a target point behind the door using the GUI to move the robot
through the door using the tripod gait while simultaneously pushing the door
open with the pedipulating foot.
Similarly, we used our controller to open a fridge door (Fig. 4B). The
friction on the foot was sufficient to open the pull door on the fridge. The
interaction force at the door did not destabilize the controller.

####  IV-A2 Transporting a Backpack

In this experiment, we collected a $2.0\text{\,}\mathrm{kg}$ backpack by
sliding the foot into one of the shoulder straps and raising the foot using
the joystick interface (Fig. 4C). We then commanded a single target point
above a box using the GUI interface and lowered the foot to release the
backpack into the box with the joystick. This experiment demonstrates the
ability of our controller to transport loads with the foot in the air thanks
to the combined loco-pedipulation capability.

####  IV-A3 Pressing a Hard-to-Reach Button

The robot can exploit the large workspace of our pedipulation controller to
reach and press a button of $0.03\text{\,}\mathrm{m}$ x
$0.05\text{\,}\mathrm{m}$ at a height of $1.25\text{\,}\mathrm{m}$ (Fig. 4D).
The operator can move the target point into the wall such that the foot exerts
a small force on the button without destabilizing the controller.

####  IV-A4 Moving an Obstacle

A common problem in legged locomotion is moving obstacles out of the way in
confined spaces (Fig. 4C). Our pedipulation controller allows the user to move
obstacles out of the robot’s way to continue walking through a confined space.
Again, robustness to unmodelled forces is required. In this experiment, we
used a box with a mass of $2.6\text{\,}\mathrm{kg}$.

####  IV-A5 Rock Sample Collection

By adding a gripper to the foot, the robot can conduct prehensile pedipulation
tasks like collecting rock samples (Fig. 4D). This task is especially relevant
in scenarios like planetary exploration, which require capable robots with
minimal mass and mechanical complexity. We successfully collected rock samples
of a size of $0.08\text{\,}\mathrm{m}$ and a mass of roughly
$300\text{\,}\mathrm{g}$. The controller proved robust to the additional mass
of the gripper and the rock.

###  IV-B Reachable Workspace and Tracking Performance

We investigated the reachable workspace of our pedipulation controller and
benchmarked the tracking performance. In these experiments, we distinguish
between two behaviors: Close-range tracking and far-range tracking. Close-
range tracking refers to target points the robot can reach without adapting
its stance or walking to reach it. Far-range tracking refers to target points
the policy reaches via locomotion or stance adaptation. Fig. 5 shows the
respective behaviors. Notably, the transition between the two is seamless. If
the target point moves outside the reachable range, the robot automatically
starts stepping to follow the target point.

Figure 5: For close-range targets, the policy does not require a stance
adaptation (A) and the tracking error quickly converges to a steady state
value (C). For far-range targets, a tripod gait emerges (B), and the tracking
error oscillates in the slow-down maneuver before converging (D).

####  IV-B1 Close-Range Target Tracking

To evaluate the workspace our controller covers without locomotion, we
deployed it in Isaac Gym and uniformly sampled commands in the robot’s
vicinity. After each episode, we discarded targets if the robot broke contact
with one of the designated stance legs or if the tracking error was higher
than $0.05\text{\,}\mathrm{m}$. As shown in Fig. 6, the whole-body behavior of
the controller allows the robot to reach a large workspace, even without
adapting its stance. Note that this workspace does not coincide with the
theoretical limits reachable through whole-body motions in stance. The robot
stretches almost to the kinematic limits but starts stepping early to reach a
more favorable configuration. These samples are discarded in this evaluation.

Additionally, we evaluated the tracking performance in the close-range
workspace by sampling points within a box of $0.3\text{\,}\mathrm{m}$ x
$0.4\text{\,}\mathrm{m}$ x $0.2\text{\,}\mathrm{m}$ both in simulation and on
the real robot. We compared the foot target position with the ground truth
position in the simulation and the estimated position from the state estimator
on the real robot. In simulation, the controller reached an average error of
$0.037\text{\,}\mathrm{m}$, while on the real robot, the average error was
$0.057\text{\,}\mathrm{m}$. We use a fixed-base inverse kinematics controller
as a baseline, which reached an average error of $0.041\text{\,}\mathrm{m}$ in
simulation on the reachable range.

####  IV-B2 Far-Range Target Tracking

To evaluate the tracking performance of the controller on target points the
policy reaches by stepping or walking, we evaluated the tracking error on the
full training range ($2.2\text{\,}\mathrm{m}$ x $2.7\text{\,}\mathrm{m}$ x
$1.3\text{\,}\mathrm{m}$) in Isaac Gym. We uniformly sampled commands on this
range and logged the tracking error at the end of each episode. Our controller
reaches an average tracking error of $0.043\text{\,}\mathrm{m}$.

###  IV-C Robustness against Disturbances

To evaluate the controller’s ability to generalize to different environmental
conditions, we deployed it on ANYmal D standing on a wet whiteboard with
extremely low friction. Although the stance legs slip during operation, the
robot does not fall. Even when applying disturbance forces at the foot, which
causes the stance legs to slip, the robot remains standing (Fig. 7A).
Additionally, we disturbed the robot’s base while the controller was tracking
a constant target point while standing on a flat floor. Thanks to the whole-
body behavior of the controller, it can compensate for disturbances on the
base. We applied disturbances of up to $132\text{\,}\mathrm{N}$. We estimated
the force on the base by summing the contact forces on the stance legs we
obtained from the state estimator via the joint torque measurements. The
disturbance force resulted in a base displacement of up to
$0.1\text{\,}\mathrm{m}$ along the heading axis. Despite this disturbance, the
tracking error only increased by $0.06\text{\,}\mathrm{m}$ (Fig. 7C).
Interestingly, the controller can compensate for disturbances in the negative
heading direction better than for those in the positive heading direction.
If we disturb the base further, interesting behaviors emerge: If required, the
stance feet adapt, and the robot steps to remain stable. The accompanying
video shows this behavior.

Figure 6: Thanks to the whole-body behavior, the robot reaches a large
workspace even without the need to adapt its stance. The blue dots represent
sampled commands the controller tracks without changing the stance
configuration and with a tracking error below $0.05\text{\,}\mathrm{m}$, shown
in top view (A) and side view (B). For reference, the orange dots are
reachable samples for a fixed-base inverse kinematics controller with the same
sampling space. Figure 7: (A): The controller can react to disturbances, even
on slippery surfaces like whiteboards. (B): When the robot’s base is
disturbed, the controller compensates for the base motion. (C): Evolution of
the tracking error over time when the base is disturbed.

##  V CONCLUSION AND FUTURE WORK

In this work, we developed a deep reinforcement learning policy to track end-
effector target points with a quadrupedal robot’s foot. We showed that such a
controller enables solving numerous real-world tasks via pedipulation: Using
our controller, we could teleoperate the robot to successfully open doors,
transport objects, press buttons, push obstacles, and collect rock samples.
These skills are enabled by the controller’s large workspace, achieved through
inherent whole-body behaviors, its loco-pedipulation capability, and its
robustness to disturbances.
While the controller is helpful in teleoperation scenarios, future work should
move towards autonomous pedipulation. For such autonomy, a hierarchical
approach, successfully used for other tasks [17], could be a valid strategy. A
hierarchical controller could contain our pedipulation policy as a low-level
policy and combine it with task-specific perceptive high-level policies that
output the foot targets. Additionally, we did not explicitly model interaction
forces. Tracking interaction forces could extend the range of admissible
manipulation tasks, such as haptic interaction or heavy payload
transportation.
Our work shows that numerous manipulation tasks can be solved by only doing
pedipulation with quadrupedal robots. This insight will be relevant for future
works on the design and control of legged mobile manipulators.

## ACKNOWLEDGMENTS

We thank Jonas Lauener for the mechanical design and control system design of
the gripper and Ennio Schnieder for the design iteration on the gripper.

## References

  * [1] J. Lee, _et al._ , “Learning quadrupedal locomotion over challenging terrain,” _Science Robotics_ , vol. 5, no. 47, p. eabc5986, 2020.
  * [2] T. Miki, _et al._ , “Learning robust perceptive locomotion for quadrupedal robots in the wild,” _Science Robotics_ , vol. 7, no. 62, p. eabk2822, 2022\.
  * [3] S. Choi, _et al._ , “Learning quadrupedal locomotion on deformable terrain,” _Science Robotics_ , vol. 8, no. 74, p. eade2256, 2023.
  * [4] D. Hoeller, _et al._ , “Anymal parkour: Learning agile navigation for quadrupedal robots,” _arXiv preprint arXiv:2306.14874_ , 2023.
  * [5] M. Hutter, _et al._ , “Towards a generic solution for inspection of industrial sites,” in _Field and Service Robotics: Results of the 11th International Conference_. Springer, 2018, pp. 575–589.
  * [6] H. Kolvenbach, _et al._ , “Towards autonomous inspection of concrete deterioration in sewers with legged robots,” _Journal of Field Robotics_ , vol. 37, no. 8, pp. 1314–1327, 2020.
  * [7] M. Stachowiak, _et al._ , “Procedures of detecting damage to a conveyor belt with use of an inspection legged robot for deep mine infrastructure,” _Minerals_ , vol. 11, no. 10, p. 1040, 2021.
  * [8] M. Tranzatto, _et al._ , “Cerberus in the darpa subterranean challenge,” _Science Robotics_ , vol. 7, no. 66, p. eabp9742, 2022.
  * [9] P. Arm, _et al._ , “Scientific exploration of challenging planetary analog environments with a team of legged robots,” _Science robotics_ , vol. 8, no. 80, p. eade9548, 2023.
  * [10] B. Lindqvist, _et al._ , “Multimodality robotic systems: Integrated combined legged-aerial mobility for subterranean search-and-rescue,” _Robotics and Autonomous Systems_ , vol. 154, p. 104134, 2022.
  * [11] C. D. Bellicoso, _et al._ , “Alma-articulated locomotion and manipulation for a torque-controllable robot,” in _2019 International conference on robotics and automation (ICRA)_. IEEE, 2019, pp. 8477–8483.
  * [12] S. Zimmermann, _et al._ , “Go fetch!-dynamic grasps using boston dynamics spot with external robotic arm,” in _2021 IEEE International Conference on Robotics and Automation (ICRA)_. IEEE, 2021, pp. 4488–4494.
  * [13] M. Mittal, _et al._ , “Articulated object interaction in unknown scenes with whole-body mobile manipulation,” in _2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)_. IEEE, 2022, pp. 1647–1654.
  * [14] H. Ferrolho, _et al._ , “Roloma: Robust loco-manipulation for quadruped robots with arms,” _arXiv preprint arXiv:2203.01446_ , 2022.
  * [15] J.-P. Sleiman, _et al._ , “Versatile multicontact planning and control for legged loco-manipulation,” _Science Robotics_ , vol. 8, no. 81, p. eadg5014, 2023.
  * [16] A. Pets, “Dogs and cats opening doors,” https://www.youtube.com/watch?v=MGGFNO3TVpg, online; accessed 06-September-2023.
  * [17] S. Jeon, _et al._ , “Learning whole-body manipulation for quadrupedal robot,” _arXiv preprint arXiv:2308.16820_ , 2023.
  * [18] O. Nachum, _et al._ , “Multi-agent manipulation via locomotion using hierarchical sim2real,” _arXiv preprint arXiv:1908.05224_ , 2019.
  * [19] M. J. Mataric, _et al._ , “Cooperative multi-robot box-pushing,” in _Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots_ , vol. 3. IEEE, 1995, pp. 556–561.
  * [20] B. Lu, _et al._ , “Autonomous obstacle legipulation with a hexapod robot,” _arXiv preprint arXiv:2011.06227_ , 2020.
  * [21] Y. Ji, _et al._ , “Hierarchical reinforcement learning for precise soccer shooting skills using a quadrupedal robot,” in _2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)_. IEEE, 2022, pp. 1479–1486.
  * [22] F. Shi, _et al._ , “Circus anymal: A quadruped learning dexterous manipulation with its limbs,” in _2021 IEEE International Conference on Robotics and Automation (ICRA)_. IEEE, 2021, pp. 2316–2323.
  * [23] H. Kolvenbach, _et al._ , “Haptic inspection of planetary soils with legged robots,” _IEEE Robotics and Automation Letters_ , vol. 4, no. 2, pp. 1626–1632, 2019.
  * [24] T. T. Topping, _et al._ , “Quasi-static and dynamic mismatch for door opening and stair climbing with a legged robot,” in _2017 IEEE International Conference on Robotics and Automation (ICRA)_. IEEE, 2017, pp. 1080–1087.
  * [25] P. Fankhauser, _et al._ , “Free gait—an architecture for the versatile control of legged robots,” in _2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)_. IEEE, 2016, pp. 1052–1058.
  * [26] X. Cheng, _et al._ , “Legs as manipulator: Pushing quadrupedal agility beyond locomotion,” _arXiv preprint arXiv:2303.11330_ , 2023.
  * [27] A. Roennau, _et al._ , “Lauron v: A versatile six-legged walking robot with advanced maneuverability,” in _2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics_. IEEE, 2014, pp. 82–87.
  * [28] G. Heppner, _et al._ , “Versatile-high power gripper for a six legged walking robot,” in _Mobile Service Robotics_. World Scientific, 2014, pp. 461–468.
  * [29] W. Brinkmann, _et al._ , “Enhancement of the six-legged robot mantis for assembly and construction tasks in lunar mission scenarios within a multi-robot team,” in _Proceedings: International Symposium on Artificial Intelligence, Robotics and Automation in Space_ , 2020.
  * [30] Y. Tsvetkov and S. Ramamoorthy, “A novel design and evaluation of a dactylus-equipped quadruped robot for mobile manipulation,” in _2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)_. IEEE, 2022, pp. 1633–1638.
  * [31] A. Agarwal, _et al._ , “Legged locomotion in challenging terrains using egocentric vision,” in _Conference on Robot Learning_. PMLR, 2023, pp. 403–415.
  * [32] N. Rudin, _et al._ , “Learning to walk in minutes using massively parallel deep reinforcement learning,” in _Conference on Robot Learning_. PMLR, 2022, pp. 91–100.
  * [33] V. Makoviychuk, _et al._ , “Isaac gym: High performance gpu-based physics simulation for robot learning,” _arXiv preprint arXiv:2108.10470_ , 2021\.
  * [34] J. Schulman, _et al._ , “Proximal policy optimization algorithms,” _arXiv preprint arXiv:1707.06347_ , 2017.
  * [35] ANYbotics, “Anymal specifications,” https://www.anybotics.com/anymal-autonomous-legged-robot/, online; accessed 27-January-2023.
  * [36] J. Hwangbo, _et al._ , “Learning agile and dynamic motor skills for legged robots,” _Science Robotics_ , vol. 4, no. 26, p. eaau5872, 2019.
  * [37] S. Khattak, _et al._ , “Complementary multi–modal sensor fusion for resilient robot pose estimation in subterranean environments,” in _2020 International Conference on Unmanned Aircraft Systems (ICUAS)_. IEEE, 2020, pp. 1024–1029.