DemoDiffusion: One-Shot Human Imitation using pre-trained Diffusion Policy

• URL: http://arxiv.org/abs/2506.20668v1
• Date: Wed, 25 Jun 2025 17:59:01 GMT
• Title: DemoDiffusion: One-Shot Human Imitation using pre-trained Diffusion Policy
• Authors: Sungjae Park, Homanga Bharadhwaj, Shubham Tulsiani,
• Abstract summary: We propose DemoDiffusion, a simple and scalable method for enabling robots to perform manipulation tasks in natural environments.<n>Our approach is based on two key insights. First, the hand motion in a human demonstration provides a useful prior for the robot's end-effector trajectory.<n>Second, while this retargeted motion captures the overall structure of the task, it may not align well with plausible robot actions in-context.
• Score: 33.18108154271181
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose DemoDiffusion, a simple and scalable method for enabling robots to perform manipulation tasks in natural environments by imitating a single human demonstration. Our approach is based on two key insights. First, the hand motion in a human demonstration provides a useful prior for the robot's end-effector trajectory, which we can convert into a rough open-loop robot motion trajectory via kinematic retargeting. Second, while this retargeted motion captures the overall structure of the task, it may not align well with plausible robot actions in-context. To address this, we leverage a pre-trained generalist diffusion policy to modify the trajectory, ensuring it both follows the human motion and remains within the distribution of plausible robot actions. Our approach avoids the need for online reinforcement learning or paired human-robot data, enabling robust adaptation to new tasks and scenes with minimal manual effort. Experiments in both simulation and real-world settings show that DemoDiffusion outperforms both the base policy and the retargeted trajectory, enabling the robot to succeed even on tasks where the pre-trained generalist policy fails entirely. Project page: https://demodiffusion.github.io/

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