Generalist robot policies improve across many tasks during use
Reinforcement Learning Enables Scaling Generalist Robot Policy Improvement
Machine LearningRobotics
Summary
Robots can learn to do many different jobs using a single large model, but they usually need extra training to get really good at each task. The authors explored whether a robot can get better at many jobs at the same time while it’s being used, by improving itself through a type of trial-and-error learning called reinforcement learning. They found that this kind of on-the-fly improvement works for multiple tasks and can even help robots perform well on new tasks they weren’t trained on before. Their experiments show that the right training methods and learning techniques are key to making this happen.
What this means in practice
- •For robotics engineers: Improve robots’ performance on many tasks simultaneously during deployment without retraining each task individually.
- •For industrial automation teams: Update robot control policies on the factory floor in real time to adapt to new or changing tasks without manual reprogramming.
Authors
Michal Korniak, Marco Bagatella, Ralf Römer, Michal Nauman, Yarden As, Pranav Vignesh, Manan Agarwal, Leander Diaz-Bone, Piotr Zalewski, Mikołaj Czarnecki, Lenart Treven, Shashwat Saxena, Sreyas Venkataraman, Sarvesh Patil, Zhiyuan Zhou, Daniel Marta, Oier Mees, Nur Muhammad Mahi Shafiullah, Carmelo Sferrazza, Bhavya Sukhija, Angela P. Schoellig, Andreas Krause, Max Simchowitz, Andrew Wagenmaker
Abstract
The introduction of generalist robot policies has enabled a single, large model to learn to perform a variety of tasks from a vast pre-training corpus. While these models often exhibit powerful zero-shot generalization capabilities, they typically require an adaptation phase to achieve sufficient performance in deployment. Iterated self-improvement via reinforcement learning (RL) has demonstrated promise in enabling this adaptation, yet such approaches have been largely explored in single-task settings - where the goal is to simply improve on a narrow deployment task - which lies in stark contrast to the multi-task capabilities of the base policy. In this work we ask whether generalist robot policies are capable of multi-task, generalist improvement in deployment - targeting many diverse tasks simultaneously - and what algorithmic recipe enables this. To answer this question, we first develop a set of structured evaluations to test generalist improvement capabilities, and systematically evaluate algorithmic and practical design choices regarding value estimation, policy extraction and resource allocation. We show that not only can generalist policies be improved online across diverse tasks, but that the right recipe can enable generalization and increase performance on tasks not considered during online training. We characterize when this generalization occurs and show that both the training task distribution and the use of value-based RL are critical. To the best of our knowledge, this is among the first evidence for online generalist improvement for robot foundation models.