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Diffusion-PbD: Generalizable Robot Programming by Demonstration with Diffusion Features
Abstract
Programming by Demonstration (PbD) is an intuitive technique for programming robot manipulation skills by demonstrating the desired behavior. However, most existing approaches either require extensive demonstrations or fail to generalize beyond their initial demonstration conditions. We introduce Diffusion-PbD, a novel approach to PbD that enables users to synthesize generalizable robot manipulation skills from a single demonstration by utilizing the representations captured by pre-trained visual foundation models. At demonstration time, hand and object detection priors are used to extract waypoints from the human demonstrations anchored to reference points in the scene. At execution time, features from pre-trained diffusion models are leveraged to identify corresponding reference points in new observations. We validate this approach through a series of real-world robot experiments, showing that Diffusion-PbD is applicable to a wide range of manipulation tasks and has strong ability to generalize to unseen objects, camera viewpoints, and scenes.
Video
Approach Overview
Diffusion-Pbd composes a mixture of pre-trained web-scale foundation models to both extract salient structure from demonstration videos and to transfer that structure to new scenes. The approach is composed of three main phases: (a) human and object detection, (b) waypoint extraction, (c) skill execution. In the first phase, we pre-process the demonstration frames by detecting human hands and their interactions with objects in the scene. Next, we map these detections to waypoints and robot gripper configurations. We anchor the waypoints relative to observation-centric reference points. This representation allow us to map the skill to new scenes by finding corresponding reference points in the new observations.
Generalization to Unseen Objects, Viewpoints, and Scenes
Wide Variety of Manipulation Skills
Contribution of Stable Diffusion Features
