Learning Reward Functions for Robotic Manipulation by Observing Humans

• URL: http://arxiv.org/abs/2211.09019v1
• Date: Wed, 16 Nov 2022 16:26:48 GMT
• Title: Learning Reward Functions for Robotic Manipulation by Observing Humans
• Authors: Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal, Jean Ponce and Cordelia Schmid
• Abstract summary: We use unlabeled videos of humans solving a wide range of manipulation tasks to learn a task-agnostic reward function for robotic manipulation policies. The learned rewards are based on distances to a goal in an embedding space learned using a time-contrastive objective.
• Score: 92.30657414416527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Observing a human demonstrator manipulate objects provides a rich, scalable and inexpensive source of data for learning robotic policies. However, transferring skills from human videos to a robotic manipulator poses several challenges, not least a difference in action and observation spaces. In this work, we use unlabeled videos of humans solving a wide range of manipulation tasks to learn a task-agnostic reward function for robotic manipulation policies. Thanks to the diversity of this training data, the learned reward function sufficiently generalizes to image observations from a previously unseen robot embodiment and environment to provide a meaningful prior for directed exploration in reinforcement learning. The learned rewards are based on distances to a goal in an embedding space learned using a time-contrastive objective. By conditioning the function on a goal image, we are able to reuse one model across a variety of tasks. Unlike prior work on leveraging human videos to teach robots, our method, Human Offline Learned Distances (HOLD) requires neither a priori data from the robot environment, nor a set of task-specific human demonstrations, nor a predefined notion of correspondence across morphologies, yet it is able to accelerate training of several manipulation tasks on a simulated robot arm compared to using only a sparse reward obtained from task completion.

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