Free Your Hands: Human-Demonstration-Free Diffusion Policy Adaptation for Robotic Manipulation

Diffusion-based policies demonstrate remarkable capabilities in generating robust and precise actions for embodied tasks. However, when adapting to a new environment with visual domain gap such as lighting variation, object appearance change, different background, and unseen distractors, these methods require a substantial amount of human demonstrations gathered through teleoperation. This data collection process incurs significant costs in terms of both time and financial resources. To this end, we introduce FreeHand, which enables cross-environment adaptation without requiring additional teleoperated demonstrations, freeing human hands from the time-consuming data collection process. Specifically, we find that a well-trained diffusion policy network struggles to adapt to new environments due to distribution mismatches at both the visual and policy levels. Simply aligning domains at the visual level is insufficient, as even subtle visual changes in the environment can lead to severe action failures. Therefore, we propose a two-level alignment scheme. The visual-level alignment is achieved through adversarial training between the visual features from old and new environments. For the policy-level alignment, we introduce a novel noise-aware adversarial learning strategy for the diffusion policy. We validate our approach through comprehensive cross-domain adaptation experiments under three settings: Sim2Sim (PushT, CALVIN, RoboTwin), Real2Sim (SimplerEnv), and Real2Real (real-world robotic deployment). Our method demonstrates significant improvement with minimal additional parameter overhead, showcasing its effectiveness and efficiency.