Learning Holistic Whole-Body Loco-Manipulation with a Bipedal Mobile Manipulator

2026-09-16

AI Summary

研究通过强化学习训练的全身控制器，使双足机器人能够协调腿部和手臂动作以完成操作任务，同时保持平衡。

Abstract

Bipedal loco-manipulation enables robots to interact with objects beyond the nominal workspace of their arms by coordinating locomotion and manipulation. Realizing this capability requires a low-level whole-body controller that translates task-level manipulation goals into coordinated arm and leg motions while maintaining balance. We present a unified whole-body controller trained with reinforcement learning that directly maps 6-DoF end-effector targets to coordinated actions for the bipedal base and robotic arm. Given only an end-effector target, the learned controller autonomously coordinates reaching, postural adaptation, and stepping without explicit base-velocity or footstep commands. A reward-gating strategy regulates the trade-offs among end-effector tracking, locomotion, and balance during training, while a temporal context estimator combines windowed Transformer encoding, recurrent GRU memory, and auxiliary dynamics prediction to extract dynamics-relevant information from observation history. Real-robot experiments demonstrate that the same controller supports reaching, postural adaptation, and stepping under commands from VR teleoperation, a learned diffusion policy, and scripted trajectories, providing a common end-effector interface for diverse manipulation tasks.

Problem

• bipedal loco-manipulation
• whole-body controller
• end-effector targets
• balance
• reinforcement learning

Innovation

• reinforcement learning
• whole-body controller
• bipedal loco-manipulation
• reward-gating strategy
• temporal context estimator