PressMimic: Pressure-Guided Motion Capture and Control for Humanoid Robot Imitation

2026-06-25

AI Summary

Existing approaches to humanoid robot imitation predominantly rely on visual and kinematic cues while neglecting contact dynamics, often resulting in foot sliding, ground penetration, and instability. This work addresses this limitation by introducing sole pressure as a unified physical grounding modality that bridges perception and control. We propose a multimodal motion capture and control framework that fuses RGB and pressure signals, featuring a pressure-guided perception model (FRAPPE++) and a pressure-supervised policy (PSP). To support this approach, we also present MotionPRO, a large-scale multimodal dataset. Our method significantly improves 3D pose estimation accuracy, trajectory consistency, and execution stability, demonstrating the critical role of pressure sensing in achieving physically consistent humanoid imitation.

Abstract

Humanoid motion imitation requires not only accurate perception of human kinematics but also faithful reproduction of physical interactions with the environment. However, existing pipelines rely primarily on vision-based motion capture and kinematic imitation, largely ignoring contact dynamics, leading to artifacts such as foot sliding, floor penetration, and unstable behaviors. In this work, we revisit humanoid motion imitation from the perspective of physical grounding and leverage pressure as a unified modality across perception and control. We present PressMimic, a framework that integrates pressure into the full pipeline from motion capture to humanoid control. In the perception stage, we introduce FRAPPE++, a multimodal model that fuses RGB and pressure to jointly estimate 3D pose and global motion, where pressure provides explicit contact and support constraints to resolve ambiguity in vision-based estimation. In the control stage, we propose a pressure-supervised policy (PSP) that incorporates pressure-derived signals into reinforcement learning, enabling physically consistent contact patterns during execution. We further construct MotionPRO, a large-scale dataset with synchronized RGB, pressure, and motion capture data. Experiments show that pressure improves motion estimation accuracy, trajectory consistency, and execution stability. These results demonstrate that pressure serves as an effective physical grounding signal, bridging perception and control for physically consistent humanoid motion imitation.

Problem

• humanoid motion imitation
• contact dynamics
• pressure sensing
• physical grounding
• motion capture

Innovation

• pressure sensing
• humanoid imitation
• physical grounding
• multimodal perception
• contact-consistent control