Progress Reward Modeling for Robotic Learning: A Comprehensive Survey
Robotic learning takes place in dynamic environments with large behavior spaces. A terminal success signal only tells the robot whether the task is completed. It does not explain whether the current behavior is making progress, remaining unchanged, or undoing earlier progress. For this reason, recent studies have increasingly explored progress rewards that provide feedback during task execution. However, the current literature lacks a shared framework. Existing methods use different observations, goal specifications, output signals, supervision sources, and evaluation protocols. This makes it difficult to compare them and understand what their results actually validate. In this survey, we...
| Authors | Jianshu Zhang, Keliang Wu, Haoran Lu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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