ConCent: Contact-Centric Real-to-Sim-to-Real Learning from One Demonstration

2026-06-29

AI Summary

This work addresses the challenge of contact-intensive manipulation tasks failing during sim-to-real transfer due to discrepancies in contact dynamics. To bridge this gap, the authors propose a contact-centric real-to-sim-to-real reinforcement learning framework that jointly models task-relevant contact event sequences and local contact dynamics from real-world demonstrations. By optimizing the contact geometry of objects in simulation, the method aligns simulated state transitions with their real-world counterparts and automatically generates structured reward signals without manual task-specific reward engineering. Experimental results demonstrate that the proposed approach significantly outperforms unconstrained reinforcement learning baselines across multiple contact-intensive tasks, achieving more stable and robust high-fidelity policy transfer.

Abstract

Sim-to-real policy transfer -- deploying policies trained in simulation in the real world -- is a promising paradigm for scaling robot manipulation without large-scale real-world data. However, transferring simulation-trained policies remains challenging due to discrepancies in contact dynamics -- particularly in contact-rich tasks where subtle differences can alter task outcomes entirely. Because interaction between the manipulated object and the environment is mediated through contact, task success depends on accurately reproducing task-relevant contacts. Accordingly, in manipulation, contact-centric fidelity -- reproducing both the contact event sequence (when, where, and how contacts occur) and the local contact dynamics (how forces and motions evolve at each contact) -- is a necessary condition for task success. Based on this insight, we propose a contact-centric real-to-sim-to-real RL framework that uses task-relevant contact event sequences extracted from real demonstrations as the learning objective. We approximate objects as groups of primitives and optimize their contact geometry in simulation so that the resulting local contact dynamics explain the observed state transitions. The contact event sequence is automatically extracted by replaying the demonstration. This sequence serves as a structured reward signal, guiding the policy toward physically plausible contact regimes validated in reality and preventing exploitation of unrealistic simulator contacts. The signal is obtained automatically, requiring no per-task reward design. Experiments on contact-rich manipulation tasks demonstrate more stable and robust sim-to-real policy transfer compared to unconstrained RL baselines.

Problem

• sim-to-real transfer
• contact dynamics
• robot manipulation
• contact-rich tasks
• policy transfer

Innovation

• contact-centric learning
• sim-to-real transfer
• contact dynamics
• real-to-sim-to-real
• manipulation policy