Zeva: In-Context Causal Learning for Generalizable Embodied Manipulation
Research
Source: arXiv cs.ROPublish time unverified
arXiv:2608.30880v1 Announce Type: new Abstract: Generalizable embodied manipulation remains difficult to achieve through pretraining alone, due to unseen physical conditions in the real world. We argue that robots need to learn from their own physical interactions on the fly during real-world deployment and use this knowledge to inform subsequent actions.