Stage-Transition Dense Reward Modeling for Reinforcement Learning
Reinforcement learning for long-horizon robotic manipulation is often limited by sparse and delayed rewards, while manually designing dense shaping signals is costly and brittle to changes in environments and object configurations. This work proposes Stage-Transition Dense Reward (STDR), a visual reward-learning framework that converts unstructured expert videos into logically grounded dense rewards for training RL agents from scratch. STDR leverages semantic understanding to infer a task's stage structure from demonstrations, and delivers two complementary learning signals during online training: (i) stage-transition feedback that provides goal-directed reward, and (ii) within-stage prog...
| Authors | Yang Yang, Bingjie Chen, Zihan Wang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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