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Robots can now ask humans precisely *what* they don't understand, leading to 30.8% better reward recovery with fewer queries.
Language observations can be transformed into precise spatial distributions, enhancing robot navigation in complex environments.
Instead of just preventing harmful actions by LM agents, we can now steer them back from the brink using human-aligned recovery plans, significantly improving safety after a mistake.
Users prefer robots that learn their preferences using CMA-ES-IG because it suggests more perceptually distinct and informative behaviors to rank.