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This research advances bipedal loco-manipulation by introducing a unified reinforcement learning controller that autonomously coordinates locomotion, postural adaptation, and arm reaching based solely on end-effector targets. By integrating Transformer encoding with GRU memory for temporal context estimation, the system effectively balances dynamic trade-offs between tracking accuracy, movement stability, and object interaction without explicit low-level commands. Real-robot validation confirms the approach's robustness across diverse inputs, including VR teleoperation and diffusion policies, marking a significant step toward versatile mobile manipulators.
Learning Holistic Whole-Body Loco-Manipulation with a Bipedal Mobile Manipulator
arXiv:2609.18930v1 Announce Type: new 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.
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.
Robotics (cs.RO)
arXiv:2609.18930 [cs.RO]
(or arXiv:2609.18930v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.18930 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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