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Get the performance boost of expensive sampling-based RL policies for a fraction of the compute by learning to prune action candidates early in the diffusion denoising process.
Runners stick to their pace 60% better and enjoy the workout more when coached by a robot dog than when using an Apple Watch.
A robot can now play recognizable piano songs after just 30 minutes of real-world training, closing the sim-to-real gap for high-precision bimanual manipulation.
RADAR offers a scalable, interpretable framework for understanding robot policy generalization by directly linking test-time performance to the training data, revealing the specific types of generalization required.
Q-functions and implicit policy extraction are game-changers for batch online RL in robotics, unlocking significant performance gains over imitation-based approaches.
