KPI: A Promptable Kernel for Physical Interaction on Humanoids

Yikai Wang, Honghao Zhu, Xiao Hu, Hao Zhang, Zelin Wang, Yip Fun Yeung, Ding Zhao, Lingfeng Sun · N/A · 2026

Summary

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.

Abstract Summary

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.

Key Points

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.

Method

KPI makes the controller’s interaction response an explicit part of the command interface. Alongside a nominal reference, the commander supplies a contract stating the interaction objectives. The kernel adapts the controller parameters toward it during execution and returns the interaction state.

We describe the contract-driven adaptation, its realization on a humanoid, and its use by a vision-language agent; a teleoperator or a VLA policy uses the same interface. Controller parameters and online optimization.

Method — KPI: A Promptable Kernel for Physical Interaction on Humanoids — Fig. 3: Task coverage: bimanual transport, articulated-object manipulation and surface interaction.

Experiments

KPI supports three task categories (Fig. 3 ): bimanual transport (box placement and carrying during kneeling, standing, and running); articulated-object manipulation (drawer and door opening, and winch operation); and surface interaction (board wiping and writing). We evaluate zero-shot agentic execution on winch operation, door passage, and box transport and placement.

Sources and Demonstrations

Figures are reproduced from Wang et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.