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.

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Repositório: ADu2021/skillXiv
Última atividade na origem: 24 de março de 2026 às 19:42
Idioma detectado do SKILL.md: inglês
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name	robo-dopamine-rl
title	Robo-Dopamine: General Process Reward Modeling for Robotic Manipulation
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2512.23703
keywords	["robotics","reinforcement-learning","reward-modeling","multi-view","process-rewards"]
description	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.

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

robo-dopamine-rl

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Overview

Core Technique

Performance

References

Overview

Core Technique

Performance

References