Continually Self-Improving Robots

Developing general-purpose robots capable of autonomously performing diverse tasks in unstructured environments remains a central challenge in robotics. Recent demonstration-driven approaches, including learning from teleoperated robot demonstrations and human video demonstrations, have provided robots with strong behavioral priors and capable initial policies. However, imitation alone is unlikely to close the gap between broad initial competence and reliable performance across the long tail of real-world tasks, objects, and environments. Robots must improve beyond their demonstrated experience: autonomously collecting data, discovering failures, adapting to new situations, and refining their skills through interaction.

This workshop advocates for an era of experiences in robot learning, where robots continually expand their capabilities by interacting with the world. We aim to bring together researchers across robot learning, reinforcement learning, and continual learning to define the algorithmic and systems-level foundations for self-improving robots.