arXiv cs.AIPaper
EmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution
Robot foundation models hit a wall when deployed to new conditions, and collecting more training data is expensive. This work flips the scaling problem: instead of collecting data to improve the model, use the model to decide what data to collect. The value-of-information framing is smart. For anyone shipping embodied AI, this is the kind of continual learning loop that makes the model useful at scale.