RARM: Confidence-Gated Progress Reward Modeling for RL in Manipulation
产业动态
来源：arXiv cs.RO发布时间待核实
arXiv:2606.22027v3 Announce Type: replace Abstract: Reinforcement learning for robot manipulation is often bottlenecked by reward design, especially in long-horizon tasks: sparse success rewards provide weak supervision, while hand-crafted dense rewards are tedious to design and generalize poorly across tasks.