A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Control
Addresses a fundamental scaling bottleneck in RL that affects robotics researchers working with simulation at scale.
AI Summary
Researchers introduce Success Guided Sampling (SGS) to improve massive-scale reinforcement learning for robotics by focusing on task configurations at the policy's capability frontier.
Excerpt
General-purpose robots must perform a wide range of tasks from agile locomotion to dexterous manipulation. While sim-to-real reinforcement learning (RL) has proven to be a useful tool for this goal, current RL pipelines depend on engineering-heavy, per-task structural priors such as shaped rewards and demonstrations. Recent work has shown that diverse simulator resets, combined with massively parallel simulation, can alleviate much of this engineering burden on several manipulation problems. How