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Simply feeding more history to visuomotor policies hurts performance; GMP solves this by learning when and what to remember, boosting success rates by 30% on memory-intensive robotic tasks.
Runners stick to their pace 60% better and enjoy the workout more when coached by a robot dog than when using an Apple Watch.
Scaling robot learning with human data isn't a simple "more is better" equation; alignment with robot learning objectives is key.
Turn your robot's clumsy pre-trained behaviors into expert-level skills with DICE-RL, a surprisingly stable and efficient RL fine-tuning method.
