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One million parallel environments are insufficient without intelligent sampling: Success Guided Sampling unlocks mega-scale RL for robots by focusing training on the edge of learned capabilities.
By constraining reward functions to Control Barrier Functions, this approach achieves safe adversarial imitation learning that adapts directly from expert observations without requiring labeled data.
Robots can now learn manipulation tasks from scratch in under an hour using only visual observations and interaction, outperforming traditional IRL and RL methods in sample efficiency.
Robots can now navigate more reliably and across different bodies (wheeled vs. legged) thanks to a hierarchical model that separates high-level planning from low-level physical constraints.
