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

arXiv · AI, language, vision and robotics · article · Sep 16, 2026 · UTC

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

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