# Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Study

arXiv: https://arxiv.org/abs/2204.12844 · HTML: https://mlsanity.com/paper/2204.12844/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 27 Apr 2022.

## Builds on (most cited first, 14 of 14)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [Distributed Prioritized Experience Replay](https://mlsanity.com/paper/1803.00933/) (2018)
- [Active Domain Randomization](https://mlsanity.com/paper/1904.04762/) (2019)
- [Learning by Playing - Solving Sparse Reward Tasks from Scratch](https://mlsanity.com/paper/1802.10567/) (2018)
- [GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning](https://mlsanity.com/paper/1810.05762/) (2018)
- [Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots](https://mlsanity.com/paper/1301.4862/) (2013)
- [Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization](https://mlsanity.com/paper/2002.11635/) (2020)
- [Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks](https://mlsanity.com/paper/2004.14404/) (2020)
- [Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2008.10224/) (2020)
- [Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learning](https://mlsanity.com/paper/2002.02697/) (2020)
- [A Learning Approach to Robot-Agnostic Force-Guided High Precision Assembly](https://mlsanity.com/paper/2010.08052/) (2020)

## Built on by (most cited first, 2 of 2)

- [Safety-Driven Deep Reinforcement Learning Framework for Cobots: A Sim2Real Approach](https://mlsanity.com/paper/2407.02231/) (2024)
- [Failure Forecasting Boosts Robustness of Sim2Real Rhythmic Insertion Policies](https://mlsanity.com/paper/2507.06519/) (2025)

## 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.