Perceptive Pedipulation with Local Obstacle Avoidance
OPEN ACCESS
Loading...
Author / Producer
Date
2024
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
Abstract
Pedipulation leverages the feet of legged robots for mobile manipulation, eliminating the need for dedicated robotic arms. While previous works have showcased blind and task-specific pedipulation skills, they fail to account for static and dynamic obstacles in the environment. To address this limitation, we introduce a reinforcement learning-based approach to train a whole-body obstacle-aware policy that tracks foot position commands while simultaneously avoiding obstacles. Despite training the policy in only five different static scenarios in simulation, we show that it generalizes to unknown environments with different numbers and types of obstacles. We analyze the performance of our method through a set of simulation experiments and successfully deploy the learned policy on the ANYmal quadruped, demonstrating its capability to follow foot commands while navigating around static and dynamic obstacles. Videos of the experiments are available at sites.google.com/leggedrobotics.com/perceptive-pedipulation.
Permanent link
Publication status
published
Editor
Book title
2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids)
Journal / series
Volume
Pages / Article No.
157 - 164
Publisher
IEEE
Event
23rd IEEE-RAS International Conference on Humanoid Robots (Humanoids 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09570 - Hutter, Marco / Hutter, Marco
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication
Notes
Conference lecture on November 23, 2024.