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Robo Dopamine Rl

Overcome reward function design challenges via General Reward Model (GRM) for step-wise progress assessment. Uses multi-view observations for occlusion robustness, hop-based progress normalization, Policy-Invariant Reward Shaping—enabling 95% robot task success within 150 interactions with theoretically-grounded dense rewards.

adu2021 6042969 1.1 KB Updated
AI & ML

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Overview

Addresses reward design challenges in robotic manipulation via principled progress modeling.

Core Technique

General Reward Model:

# Hop-based progress: normalized by task span
progress = model.predict_progress(observation, task_span)

# Multi-view fusion
fused_progress = aggregate_views([view1, view2, view3])

# Policy-Invariant Reward Shaping
reward = outcome_reward + policy_invariant_shaping(fused_progress)

Performance

  • 92.8% task completion accuracy
  • 95% success within 150 interactions
  • Theoretically grounded reward shaping

References

  • Step-wise progress discretization
  • Multi-view observation fusion
  • Policy-Invariant Reward Shaping

adu2021/skillxiv/tree/main/skills/skillxiv-v0.0.2-claude-opus-4.6/robo-dopamine-rl commit 6042969663

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