Leveraging Procedural Generation for Learning Autonomous Peg-in-Hole Assembly in Space

TL;DR

This paper introduces a novel deep reinforcement learning approach utilizing procedural generation and domain randomization to enable autonomous peg-in-hole assembly in space robotics, improving adaptability and generalization in diverse scenarios.

Contribution

It presents a new simulation-based training method combining procedural generation with reinforcement learning for space robotic assembly tasks.

Findings

Agents generalize to new assembly scenarios

Procedural generation enhances robustness

Evaluation of multiple RL algorithms shows trade-offs

Abstract

The ability to autonomously assemble structures is crucial for the development of future space infrastructure. However, the unpredictable conditions of space pose significant challenges for robotic systems, necessitating the development of advanced learning techniques to enable autonomous assembly. In this study, we present a novel approach for learning autonomous peg-in-hole assembly in the context of space robotics. Our focus is on enhancing the generalization and adaptability of autonomous systems through deep reinforcement learning. By integrating procedural generation and domain randomization, we train agents in a highly parallelized simulation environment across a spectrum of diverse scenarios with the aim of acquiring a robust policy. The proposed approach is evaluated using three distinct reinforcement learning algorithms to investigate the trade-offs among various paradigms. We demonstrate the adaptability of our agents to novel scenarios and assembly sequences while emphasizing the potential of leveraging advanced simulation techniques for robot learning in space. Our findings set the stage for future advancements in intelligent robotic systems capable of supporting ambitious space missions and infrastructure development beyond Earth.