外观
论文arXiv 2609.361511 个月前
KPI：用于人形机器人物理交互的可提示内核
原文链接：KPI: A Promptable Kernel for Physical Interaction on Humanoids（arXiv 2609.36151 · 发布日期：2026-09-28）
KPI: A Promptable Kernel for Physical Interaction on Humanoids
KPI通过由轨迹源发送交互契约，自适应调整手臂控制，在人形机器人绞盘、开门、搬箱等任务上验证。
核心要点
- 问题在于单纯轨迹难以表达接触交互，通用系统常依赖预设或手选控制器。
- KPI位于轨迹源与未修改全身跟踪器之间，按方向指定跟踪、顺应或保持力范围。
- 内核利用跟踪误差和力估计，在接触频率下调整手臂刚度、阻尼、参考和前馈。
- 实验展示了指令驱动绞盘、开门、搬箱及脚本化表面交互，绞盘可吊起第二台机器人。
验证范围与局限：验证范围为人形机器人上的指令驱动绞盘、开门、搬箱和脚本化表面交互实验。
英文摘要 (Abstract)
Humanoids now walk, balance and reach with remarkable generality: one whole-body tracking policy follows references from a human, or from an end-to-end policy. That generality travels in the trajectory, and a trajectory alone carries limited information about the interaction it should produce: at contact, the executing controller determines how the robot behaves. Single-task policies usually reach hard interactions by optimising trajectory and controller together in simulation; general stacks usually assume a preset or hand-chosen controller. We present KPI, a promptable kernel for physical interaction between the trajectory source and an unmodified whole-body tracker. Instead of a controller fixed before the task, the trajectory source sends a contract: per direction, track, comply, or hold a force range. From tracking error and a wrench estimate, the kernel adapts the arms' stiffness, damping, reference and feedforward toward it at contact rate. We demonstrate KPI through an agentic framework: from one instruction, a vision-language agent writes both the reference trajectory and the contract, with no task-specific code. We demonstrate instruction-driven winch operation, door opening, and box transport, alongside scripted surface-interaction experiments. In the winch demonstration, the humanoid is able to turn a crank to hoist a second robot fully off the ground.
作者：Yikai Wang, Honghao Zhu, Xiao Hu, Hao Zhang, Zelin Wang, Yip Fun Yeung, Ding Zhao, Lingfeng Sun
来源
本页事实对应的公开页面，以原文为准。