Learning to Open and Traverse Doors with a Legged Manipulator


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Date

2025

Publication Type

Conference Paper

ETH Bibliography

yes

Citations

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Data

Abstract

Using doors is a longstanding challenge in robotics and is of significant practical interest in giving robots greater access to human-centric spaces. The task is challenging due to the need for online adaptation to varying door properties and precise control in manipulating the door panel and navigating through the confined doorway. To address this, we propose a learning-based controller for a legged manipulator to open and traverse through doors. The controller is trained using a teacher-student approach in simulation to learn robust task behaviors as well as estimate crucial door properties during the interaction. Unlike previous works, our approach is a single control policy that can handle both push and pull doors through learned behaviour which infers the opening direction during deployment without prior knowledge. The policy was deployed on the ANYmal legged robot with an arm and achieved a success rate of 95.0% in repeated trials conducted in an experimental setting. Additional experiments validate the policy’s effectiveness and robustness to various doors and disturbances. A video overview of the method and experiments can be found at youtu.be/tQDZXN_k5NU.

Publication status

published

Book title

Proceedings of The 8th Conference on Robot Learning

Volume

270

Pages / Article No.

2913 - 2927

Publisher

PMLR

Event

8th Conference on Robot Learning (CoRL 2024)

Edition / version

Methods

Software

Geographic location

Date collected

Date created

Subject

Mobile Manipulation; Legged Manipulator; Reinforcement Learning

Organisational unit

09570 - Hutter, Marco / Hutter, Marco check_circle
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication

Notes

"RSL; Mobile Manipulation;Legged Manipulator;Reinforcement Learning"

Funding

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