RoboDuet: Learning a Cooperative Policy for Whole-Body Legged Loco-Manipulation

Fully leveraging the loco-manipulation capabilities of a quadruped robot equipped with a robotic arm is non-trivial, as it requires controlling all degrees of freedom (DoFs) of the quadruped robot to achieve effective whole-body coordination. In this letter, we propose a novel framework RoboDuet, which employs two collaborative policies to realize locomotion and manipulation simultaneously, achieving whole-body control through mutual interactions. Beyond enabling large-range 6D pose tracking for manipulation, we find that the two-policy framework supports zero-shot transfer across quadruped robots with similar morphology and physical dimensions in the real world. Our experiments demonstrate that RoboDuet achieves a 23% improvement in success rate over the baseline in challenging loco-manipulation tasks employing whole-body control.

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Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2025-03-14
DOI
https://doi.org/10.1109/lra.2025.3551230
Citations
3
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
3.64
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article

RoboDuet: Learning a Cooperative Policy for Whole-Body Legged Loco-Manipulation

Zhecheng Yuan, Jiangmiao Pang, Yandong Ji, Shoujie Li et al.
3 citations
IEEE Robotics and Automation Letters
Robot Manipulation and Learning
3.64
article

RoboDuet: Learning a Cooperative Policy for Whole-Body Legged Loco-Manipulation

Zhecheng Yuan, Jiangmiao Pang, Yandong Ji, Shoujie Li, Huazhe Xu, Houde Liu, Qingwei Ben, Guoping Pan, Guangqi Jiang
article en
3 citations

Abstract

Fully leveraging the loco-manipulation capabilities of a quadruped robot equipped with a robotic arm is non-trivial, as it requires controlling all degrees of freedom (DoFs) of the quadruped robot to achieve effective whole-body coordination. In this letter, we propose a novel framework RoboDuet, which employs two collaborative policies to realize locomotion and manipulation simultaneously, achieving whole-body control through mutual interactions. Beyond enabling large-range 6D pose tracking for manipulation, we find that the two-policy framework supports zero-shot transfer across quadruped robots with similar morphology and physical dimensions in the real world. Our experiments demonstrate that RoboDuet achieves a 23% improvement in success rate over the baseline in challenging loco-manipulation tasks employing whole-body control.

IEEE Robotics and Automation LettersVol. 10(5)
University of San Diego (US), Beijing Academy of Artificial Intelligence (CN), Tsinghua–Berkeley Shenzhen Institute (CN), Shanghai Artificial Intelligence Laboratory (CN), Tsinghua University (CN)
Openalex Percentile: Top 8%
Robot Manipulation and Learning
3.64
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RoboDuet: Learning a Cooperative Policy for Whole-Body Legged Loco-Manipulation — Zhecheng Yuan, Jiangmiao Pang, et al. · IEEE Robotics and Automation Letters (2025) | TGRS Research Map | TGRS