Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Study
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Abstract (arXiv)
The Reinforcement Learning (RL) paradigm has been an essential tool for automating robotic tasks. Despite the advances in RL, it is still not widely adopted in the industry due to the need for an expensive large amount of robot interaction with its environment. Curriculum Learning (CL) has been proposed to expedite learning. However, most research works have been only evaluated in simulated environments, from video games to robotic toy tasks. This paper presents a study for accelerating robot learning of contact-rich manipulation tasks based on Curriculum Learning combined with Domain Randomization (DR). We tackle complex industrial assembly tasks with position-controlled robots, such as insertion tasks. We compare different curricula designs and sampling approaches for DR. Based on this study, we propose a method that significantly outperforms previous work, which uses DR only (No CL is used), with less than a fifth of the training time (samples). Results also show that even when training only in simulation with toy tasks, our method can learn policies that can be transferred to the real-world robot. The learned policies achieved success rates of up to 86% on real-world complex industrial insertion tasks (with tolerances of ± 0.01~mm) not seen during the training.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 35 references that are in the map. First posted on arXiv 27 Apr 2022.
Builds on
14 of 35 references.
References · 14
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
- 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
- 2018Learning Dexterous In-Hand Manipulationcited by 643
- 2020Curriculum Learning for Reinforcement Learning Domains: A Framework and Surveycited by 350
- 2018Distributed Prioritized Experience Replaycited by 260
- 2018Learning by Playing - Solving Sparse Reward Tasks from Scratchcited by 164
- 2019Active Domain Randomizationcited by 161
- 2018GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learningcited by 77
- 2013Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robotscited by 56
- 2020Sim2Real Transfer for Reinforcement Learning without Dynamics Randomizationcited by 43
- 2020Meta-Reinforcement Learning for Robotic Industrial Insertion Taskscited by 28
- 2020Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approachcited by 25
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.