发表机构
Legged AI Lab, Shanghai Innovation Institute（上海创新研究院足式人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种人形机器人拉黄包车的全身控制框架，通过教师-学生蒸馏和强化学习实现重载运输，在Unitree G1上验证了稳定性和低运输成本。
AI 中文摘要
人形机器人可以通过拉动被动轮式车辆而非背负负载来运输比自身重得多的载荷。然而，这种能力产生了一个耦合运动问题：机器人必须保持持续的上半身接触，同时适应由载荷、车辆和地形引起的未知且构型相关的力。我们提出了一种用于人形机器人拉黄包车的全身控制框架，该框架在不确定的负载动力学下跟踪命令的车辆运动，同时保持平衡和稳定的抓握。在训练期间，一个特权教师利用车辆状态、交互力和负载属性。其动作和潜在变量被蒸馏到一个基于历史条件的学生中，该学生从本体感觉响应中隐式推断耦合动力学，随后进行强化学习微调。与无历史和仅历史基线的比较表明，所得策略实现了准确的车辆跟踪，同时减少了车辆振荡、躯干倾斜和执行成本。行为分析表明，Unitree G1推动黄包车并产生步态同步的全身反应，以稳定其横向和侧倾运动。此外，拉动重新分配了关节力，并在大多数测试的负载-速度条件下，产生了比空载行走更低的机器人归一化运输成本代理。在硬件上，单个策略可执行启动、持续拉动、转弯和停止，适用于刚性载荷和人类乘客，处理高达115公斤的负载黄包车质量，无需针对负载重新调整。这些结果展示了通过协调且持续的人形机器人-车辆交互实现稳健的重载运输。
英文摘要
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.
