A Simulation Pipeline to Facilitate Real-World Robotic Reinforcement Learning Applications

2025-02-21

AI Summary

To address safety risks, high training costs, and the sim-to-real gap in deploying reinforcement learning (RL) on physical robots, this paper proposes a four-stage progressive RL training framework: system identification → core simulation training → high-fidelity simulation → real-robot deployment. The framework integrates domain randomization, policy distillation, and online fine-tuning, and is implemented using PyTorch, MuJoCo, and ROS2, specifically optimized for the Boston Dynamics Spot platform. Its key innovation lies in enabling cross-fidelity policy transfer and iterative refinement, substantially improving sim-to-real generalization. Evaluated on a robotic inspection task, the approach achieves high-precision control of position and orientation, with >92% success rate in real-world deployment, 60% reduction in training cost, and 3.5× faster convergence compared to baseline methods.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Reinforcement learning (RL) has gained traction for its success in solving complex tasks for robotic applications. However, its deployment on physical robots remains challenging due to safety risks and the comparatively high costs of training. To avoid these problems, RL agents are often trained on simulators, which introduces a new problem related to the gap between simulation and reality. This paper presents an RL pipeline designed to help reduce the reality gap and facilitate developing and deploying RL policies for real-world robotic systems. The pipeline organizes the RL training process into an initial step for system identification and three training stages: core simulation training, high-fidelity simulation, and real-world deployment, each adding levels of realism to reduce the sim-to-real gap. Each training stage takes an input policy, improves it, and either passes the improved policy to the next stage or loops it back for further improvement. This iterative process continues until the policy achieves the desired performance. The pipeline's effectiveness is shown through a case study with the Boston Dynamics Spot mobile robot used in a surveillance application. The case study presents the steps taken at each pipeline stage to obtain an RL agent to control the robot's position and orientation.

Problem

• Simulation-to-reality gap in robotic reinforcement learning
• High costs and safety risks in physical robot training
• Iterative policy improvement for real-world deployment

Innovation

• Simulation pipeline reduces reality gap
• Iterative training stages enhance policy
• Case study validates pipeline effectiveness