Zeva: In-Context Causal Learning for Generalizable Embodied Manipulation
技术动态
来源：arXiv cs.RO发布时间待核实
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.