arXiv：cs.LG· Yangzhi Yang, Xiansheng Lin, Zhaoming Xie, Xiaobin Xiong·· 6 小时前AI 评分44
人形机器人拉黄包车：耦合轮式负载下的全身运动控制
Humanoid Rickshaw Pulling: Whole-Body Locomotion under Coupled Wheeled Loads
AI 导读
研究提出一套人形机器人拉黄包车的全身控制框架，通过特权教师蒸馏到历史条件学生策略并做强化学习微调，在保持平衡与稳定抓握的同时跟踪车辆运动。Unitree G1 实机用单一策略完成起步、持续拉行、转弯和停止，可拉动含载荷总质量达 115 kg 的黄包车且无需针对负载重新调参。
正文
Abstract:Humanoid robots could transport payloads substantially heavier than themselves by pulling passive wheeled vehicles instead of carrying the load. This capability, however, creates a coupled locomotion problem: the robot must maintain persistent upper-body contact while adapting to unknown, configuration-dependent forces arising from the payload, vehicle, and terrain. We present a whole-body control framework for humanoid rickshaw pulling that tracks commanded vehicle motion while preserving balance and stable grasps under uncertain load dynamics. During training, a privileged teacher exploits vehicle states, interaction forces, and load properties. Its actions and latent are distilled into a history-conditioned student that implicitly infers coupled dynamics from proprioceptive responses, followed by reinforcement-learning fine-tuning. Comparisons with \emph{No History} and \emph{Only History} baselines show that the resulting policy achieves accurate vehicle tracking while reducing vehicle oscillation, torso tilt, and actuation cost. Behavioral analysis shows that Unitree G1 propels the rickshaw and generates gait-synchronized whole-body reactions that stabilize its lateral and roll motions. Moreover, pulling redistributes joint effort and yields a lower robot-normalized cost-of-transport proxy than unloaded walking over most tested load--speed conditions. On hardware, a single policy performs starting, sustained pulling, turning, and stopping with both rigid payloads and human passengers, handling a loaded rickshaw mass of up to 115~kg without load-specific retuning. These results demonstrate robust heavy-load transportation through coordinated and persistent humanoid--vehicle interaction.
| Subjects: | Robotics (cs.RO); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.04238 [cs.RO] |
| (or arXiv:2610.04238v2 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.04238 arXiv-issued DOI via DataCite |
Submission history
From: Yangzhi Yang [view email]
[v1]
Sat, 3 Oct 2026 03:00:09 UTC (5,528 KB)
[v2]
Wed, 7 Oct 2026 13:33:39 UTC (3,779 KB)
来源：arXiv：cs.LG · arxiv.org