RM-RL: Role-Model Reinforcement Learning for Precise Robot Manipulation

2025-10-16

AI Summary

To address key challenges in high-precision robotic manipulation—including reliance on expert demonstrations, low data efficiency, and offline reinforcement learning (RL) distributional shift—this paper proposes Role-Model RL, a novel framework that eliminates the need for human demonstrations. It introduces a learnable Role-Model that generates near-optimal action labels online, transforming policy optimization into a supervision-dominated hybrid training process. This enables offline reuse of online-collected data and ensures stable convergence. By synergistically integrating the sample efficiency of supervised learning with the generalization capability of RL, the framework significantly improves training efficiency and robustness. Experiments demonstrate 53% and 20% improvements in translational and rotational precision, respectively, in real-world settings, and successful execution of fine-grained tasks such as precise cell plate placement. Role-Model RL establishes a new paradigm for demonstration-free, high-precision robotic control.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Precise robot manipulation is critical for fine-grained applications such as chemical and biological experiments, where even small errors (e.g., reagent spillage) can invalidate an entire task. Existing approaches often rely on pre-collected expert demonstrations and train policies via imitation learning (IL) or offline reinforcement learning (RL). However, obtaining high-quality demonstrations for precision tasks is difficult and time-consuming, while offline RL commonly suffers from distribution shifts and low data efficiency. We introduce a Role-Model Reinforcement Learning (RM-RL) framework that unifies online and offline training in real-world environments. The key idea is a role-model strategy that automatically generates labels for online training data using approximately optimal actions, eliminating the need for human demonstrations. RM-RL reformulates policy learning as supervised training, reducing instability from distribution mismatch and improving efficiency. A hybrid training scheme further leverages online role-model data for offline reuse, enhancing data efficiency through repeated sampling. Extensive experiments show that RM-RL converges faster and more stably than existing RL methods, yielding significant gains in real-world manipulation: 53% improvement in translation accuracy and 20% in rotation accuracy. Finally, we demonstrate the successful execution of a challenging task, precisely placing a cell plate onto a shelf, highlighting the framework's effectiveness where prior methods fail.

Problem

• Addresses precise robot manipulation challenges in fine-grained applications
• Eliminates reliance on human demonstrations through automated label generation
• Solves distribution shifts and low data efficiency in offline RL

Innovation

• Role-model strategy generates labels automatically
• Reformulates policy learning as supervised training
• Hybrid scheme leverages online data for offline reuse