Benchmarking Reinforcement Learning Methods for Dexterous Robotic Manipulation with a Three-Fingered Gripper

TL;DR

This paper benchmarks three reinforcement learning algorithms trained directly in real-world settings for dexterous robotic manipulation, highlighting practical challenges and insights for real-world application of RL in robotics.

Contribution

It demonstrates the feasibility of training RL algorithms directly in real-world environments for complex manipulation tasks, providing valuable benchmarks and methodological insights.

Findings

RL algorithms successfully trained in real-world settings

Insights into challenges of real-world RL training

Benchmark results for three RL algorithms

Abstract

Reinforcement Learning (RL) training is predominantly conducted in cost-effective and controlled simulation environments. However, the transfer of these trained models to real-world tasks often presents unavoidable challenges. This research explores the direct training of RL algorithms in controlled yet realistic real-world settings for the execution of dexterous manipulation. The benchmarking results of three RL algorithms trained on intricate in-hand manipulation tasks within practical real-world contexts are presented. Our study not only demonstrates the practicality of RL training in authentic real-world scenarios, facilitating direct real-world applications, but also provides insights into the associated challenges and considerations. Additionally, our experiences with the employed experimental methods are shared, with the aim of empowering and engaging fellow researchers and practitioners in this dynamic field of robotics.