TAO-Force: Unifying Force-Aware Perception and Fast-Slow Control for Contact-Rich Manipulation

2026-09-16

AI Summary

针对接触丰富操作中视觉感知和位置控制不足的问题，提出TAO-Force框架，通过力感知学习与接触调节执行相结合的方法来解决。

Abstract

Vision-Language-Action (VLA) models have demonstrated strong performance across diverse robotic manipulation tasks, yet their predominantly vision-centric perception and position-controlled execution remain insufficient for contact-rich manipulation. Visual observations alone often provide limited evidence of contact onset and interaction magnitude, while position-control policies cannot respond compliantly to rapidly changing contact dynamics. To bridge both the perception and control gaps, we propose TAO-Force, a force-conditioned VLA framework that combines force-aware policy learning with contact-regulated execution. For force-aware perception, TAO-Force introduces Force-conditioned Feature-wise Linear Modulation (F-FiLM) to inject encoded force feedback into the representations of a frozen pretrained visual-language backbone while preserving its semantic priors. For responsive control, it employs a contact-gated fast-slow architecture, with a slow position-control branch tracking nominal trajectories during non-contact phases and a fast admittance-control branch regulating physical interaction during contact phases. Detailed analyses on a force-perception task and real-world evaluations across four contact-rich manipulation tasks validate the effectiveness and robustness of TAO-Force.

Problem

• contact-rich manipulation
• vision-centric perception
• position-controlled execution

Innovation

• Force-conditioned Feature-wise Linear Modulation (F-FiLM)
• contact-gated fast-slow architecture
• force-aware perception