A Physics-Consistent Benchmark for Contact-Rich Human-Robot Interaction in Assistive Care

2026-09-02

AI Summary

为解决接触丰富的人机交互任务评估问题，提出了一种物理一致的基准方法，结合了可变形被动响应人体、物理感知评分和冻结视觉/评分协议。

Abstract

Conventional task-level evaluation asks whether a robot policy completes a specified action, but can miss failures that emerge only during physical human contact. This limitation is critical in contact-rich assistive tasks, where meaningful evaluation requires a physically responsive human, interaction-quality assessment beyond task success, and a leak-free observer-scorer protocol. We introduce a physics-consistent benchmark for contact-rich human-robot interaction, instantiated in robot-assisted bathing. The benchmark combines a deformable, passively responding human, physics-aware scores alongside task-level success, and a frozen vision-only / scorer-only evaluation protocol. To establish physical validity, region-wise simulated responses are calibrated against force-indentation measurements from Franka impedance pushes on a medical-care manikin. Under a frozen T1-T7 protocol with 140 runs per method, an LLM-augmented state machine (State Machine) achieves 72.9% task success but drops to 56.4% after correct-region and force-safety screening; VoxPoser produces lighter and more stable contact but completes only 27.9% of trials; and zero-shot pi0.5 achieves 0.7% task success with no correct-region or safety-gated successes. These results show that task completion alone does not imply physically valid contact and motivate physics-aware screening before deployment of contact-rich assistive robot policies.

Problem

• human-robot interaction
• contact-rich tasks
• assistive care
• physics-aware evaluation
• task success

Innovation

• physics-consistent benchmark
• contact-rich human-robot interaction
• assistive care
• deformable human model
• physics-aware scores