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 stag

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ethics.ai (30 June 2026), “Stage-Transition Dense Reward Modeling for Reinforcement Learning,” evidence record 410, https://ethics.ai/record/410 (originally published by arXiv).

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