REVIEW 6 cited by
Learning Force Control for Legged Manipulation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Controlling contact forces during interactions is critical for locomotion and manipulation tasks. While sim-to-real reinforcement learning (RL) has succeeded in many contact-rich problems, current RL methods achieve forceful interactions implicitly without explicitly regulating forces. We propose a method for training RL policies for direct force control without requiring access to force sensing. We showcase our method on a whole-body control platform of a quadruped robot with an arm. Such force control enables us to perform gravity compensation and impedance control, unlocking compliant whole-body manipulation. The learned whole-body controller with variable compliance makes it intuitive for humans to teleoperate the robot by only commanding the manipulator, and the robot's body adjusts automatically to achieve the desired position and force. Consequently, a human teleoperator can easily demonstrate a wide variety of loco-manipulation tasks. To the best of our knowledge, we provide the first deployment of learned whole-body force control in legged manipulators, paving the way for more versatile and adaptable legged robots.
Forward citations
Cited by 6 Pith papers
-
FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots
FACET trains legged robots to track a virtual mass-spring-damper reference, so the user can tune stiffness and virtual mass to control how the robot yields to or applies forces.
-
Scoop-and-Toss: Dynamic Object Collection for Quadrupedal Systems
A simulation study shows that a quadruped with a passive leg-mounted scoop and a back tray can learn to scoop objects and toss them into the tray, collecting multiple objects via a hierarchical policy.
-
Physically Consistent Humanoid Loco-Manipulation using Latent Diffusion Models
A latent-diffusion-image-to-keyframe pipeline enables whole-body trajectory optimization to solve long-horizon humanoid loco-manipulation tasks in simulation, outperforming contact-only guidance.
-
Mimicking-Bench: A Benchmark for Generalizable Humanoid-Scene Interaction Learning via Human Mimicking
Mimicking-Bench provides six humanoid-scene interaction tasks with 23K human motion references and a retarget-track-imitate pipeline that beats data-free RL on average success.
-
Bipedalism for Quadrupedal Robots: Versatile Loco-Manipulation through Risk-Adaptive Reinforcement Learning
The paper trains a quadrupedal robot to walk bipedally using a risk-adaptive distributional reinforcement learning method, and demonstrates front-leg manipulation in simulation and on a Unitree Go2.
-
Efficient Learning of A Unified Policy For Whole-body Manipulation and Locomotion Skills
Adding a physical feasibility reward derived from inverse kinematics to a unified whole-body RL policy improves loco-manipulation accuracy and prevents the policy from abandoning arm coordination during locomotion training.
Discussion (0). Continue with ORCID to comment.