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cs.RO2026
Latent Policy Steering: An Efficient and Flexible Framework for Cross-Embodiment Transfer
Yiqi Wang, Mrinal Verghese, Jeff Schneider
The performance of learned robot visuomotor policies depends heavily on the size and quality of their training data, yet collecting high-quality demonstrations remains costly for r…
cs.LG2026
Retrospective In-Context Learning for Temporal Credit Assignment with Large Language Models
Wen-Tse Chen, Jiayu Chen, Fahim Tajwar +4
Learning from self-sampled data and sparse environmental feedback remains a fundamental challenge in training self-evolving agents. Temporal credit assignment mitigates this issue…