ContactMimic: Humanoid Object Interaction via Contact Control

2026-07-09

AI Summary

This work addresses the challenge of performing precise physical interaction tasks—such as sitting on a chair or pushing furniture—with humanoid robots, which cannot be reliably achieved through keypoint tracking alone. The authors propose a novel approach that decouples geometric pose from contact behavior, enabling explicit control over contact initiation or suppression via part-level binary contact commands jointly optimized with keypoint trajectories. The method incorporates a contact-following reward and trajectory augmentation strategy, facilitating diverse physical interactions without requiring task-specific reward engineering and supporting effective sim-to-real transfer. In simulation, the approach outperforms pure keypoint-based methods across ten distinct tasks, and real-world experiments successfully demonstrate controllable contact execution in five of these tasks.

Abstract

Keypoint tracking alone is insufficient for object interaction tasks such as sitting on a chair, wiping a board, or pushing furniture, where the robot can reach the correct pose without making meaningful physical contact with the object. We present CONTACTMIMIC, a learning framework that tracks explicit partlevel binary contact commands alongside keypoint trajectories. CONTACTMIMIC is made possible through the use of contact-following rewards and a trajectory augmentation scheme aimed at breaking the correlations between keypoint trajectories and contact labels. The resulting policy successfully decouples contact behavior from keypoint geometry, and achieves precise physical contact as well as contact-controllability (produce or suppress contact during deployment as desired). Simulation experiments across 10 diverse human-object interaction motions confirm that CONTACTMIMIC exhibits contact controllability that enables it to complete manipulation tasks without task-specific rewards, while also outperforming keypoint-only trackers on contact-relevant tasks. Ablations confirm the necessity of the proposed trajectory augmentation scheme and sim2real deployment validates contact controllability in the real world across 5 different motions. Video results are available on https://lixinyao11.github.io/contactmimic-page/.

Problem

• humanoid object interaction
• physical contact
• keypoint tracking
• contact control
• contact controllability

Innovation

• contact control
• keypoint tracking
• trajectory augmentation
• humanoid manipulation
• contact controllability