Experience from deployment offers an opportunity for robots to improve policy reliability through learning from their own executions. Collective experience learning (CEL) is introduced, where deployed robots share execution experience to improve a common policy. CEL is realized through failure-guided experience retrieval fine-tuning (FERT), which uses failure contexts to retrieve relevant trajectory segments from the fleet's shared successful experience and uses their actions as supervision for policy updates without additional expert demonstrations.
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This could be useful for scalable robot learning, allowing improved policy reliability without the need for additional expert corrective actions.
We study how vision-language models (VLMs) can learn to think and actively explore in 3D space. Instead of providing multi-view images, videos, or 3D representations to the model, we preserve the native visual encoding of VLMs, i.e., a single image, one at a time, just like humans, and treat the 3D representation only as an environment with which the model can interact through actions (e.g., move left) and observations (rendered images).
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4 days agoDaily PapersWaypoint-OPD uses an intermediate teacher checkpoint to compare early and late shifts in action preferences during policy transfer. It evaluates the consistency between early and late shifts and suppresses the endpoint shift when they conflict.
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6 days ago