Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation

• URL: http://arxiv.org/abs/2512.23703v1
• Date: Mon, 29 Dec 2025 18:57:44 GMT
• Title: Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation
• Abstract summary: We introduce Dopamine-Reward, a novel reward modeling method for learning a process reward model from multi-view inputs.<n>At its core is our General Reward Model (GRM), trained on a vast 3,400+ hour dataset.<n>Building upon Dopamine-Reward, we propose Dopamine-RL, a robust policy learning framework.
• Score: 42.7004446545722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The primary obstacle for applying reinforcement learning (RL) to real-world robotics is the design of effective reward functions. While recently learning-based Process Reward Models (PRMs) are a promising direction, they are often hindered by two fundamental limitations: their reward models lack step-aware understanding and rely on single-view perception, leading to unreliable assessments of fine-grained manipulation progress; and their reward shaping procedures are theoretically unsound, often inducing a semantic trap that misguides policy optimization. To address these, we introduce Dopamine-Reward, a novel reward modeling method for learning a general-purpose, step-aware process reward model from multi-view inputs. At its core is our General Reward Model (GRM), trained on a vast 3,400+ hour dataset, which leverages Step-wise Reward Discretization for structural understanding and Multi-Perspective Reward Fusion to overcome perceptual limitations. Building upon Dopamine-Reward, we propose Dopamine-RL, a robust policy learning framework that employs a theoretically-sound Policy-Invariant Reward Shaping method, which enables the agent to leverage dense rewards for efficient self-improvement without altering the optimal policy, thereby fundamentally avoiding the semantic trap. Extensive experiments across diverse simulated and real-world tasks validate our approach. GRM achieves state-of-the-art accuracy in reward assessment, and Dopamine-RL built on GRM significantly improves policy learning efficiency. For instance, after GRM is adapted to a new task in a one-shot manner from a single expert trajectory, the resulting reward model enables Dopamine-RL to improve the policy from near-zero to 95% success with only 150 online rollouts (approximately 1 hour of real robot interaction), while retaining strong generalization across tasks. Project website: https://robo-dopamine.github.io

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