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
Record details
Published: 21 July 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 28 July 2026
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ethics.ai (21 July 2026), “Progress Reward Modeling for Robotic Learning: A Comprehensive Survey,” evidence record 13766, https://ethics.ai/record/13766 (originally published by HuggingFace Daily Papers).
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