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Allocating extra computation during decision-making boosts robot manipulation success rates, revealing a critical strategy for handling complex tasks.
Generalist robot policies can achieve 95% success rates on real-world manipulation tasks by continually learning from a fleet of robots, even in the face of distribution shifts and long-tail failures.
