# Dual-Arm Adversarial Robot Learning

arXiv: https://arxiv.org/abs/2110.08066 · HTML: https://mlsanity.com/paper/2110.08066/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 20 references that are in the map.
- First posted on arXiv 15 Oct 2021.

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

- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [A Study on Overfitting in Deep Reinforcement Learning](https://mlsanity.com/paper/1804.06893/) (2018)
- [Learning to Walk via Deep Reinforcement Learning](https://mlsanity.com/paper/1812.11103/) (2018)
- [Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks](https://mlsanity.com/paper/1810.10191/) (2018)
- [Learning Contact-Rich Manipulation Skills with Guided Policy Search](https://mlsanity.com/paper/1501.05611/) (2015)
- [Robust Policies via Mid-Level Visual Representations: An Experimental Study in Manipulation and Navigation](https://mlsanity.com/paper/2011.06698/) (2020)
- [AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers](https://mlsanity.com/paper/1909.04121/) (2019)
- [Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learning](https://mlsanity.com/paper/2002.02697/) (2020)
- [Learning Vision-based Reactive Policies for Obstacle Avoidance](https://mlsanity.com/paper/2010.16298/) (2020)
- [Learning to Centralize Dual-Arm Assembly](https://mlsanity.com/paper/2110.04003/) (2021)
- [Reinforcement Learning Experiments and Benchmark for Solving Robotic Reaching Tasks](https://mlsanity.com/paper/2011.05782/) (2020)
- [Making Curiosity Explicit in Vision-based RL](https://mlsanity.com/paper/2109.13588/) (2021)

## Abstract (arXiv)

Robot learning is a very promising topic for the future of automation and machine intelligence. Future robots should be able to autonomously acquire skills, learn to represent their environment, and interact with it. While these topics have been explored in simulation, real-world robot learning research seems to be still limited. This is due to the additional challenges encountered in the real-world, such as noisy sensors and actuators, safe exploration, non-stationary dynamics, autonomous environment resetting as well as the cost of running experiments for long periods of time. Unless we develop scalable solutions to these problems, learning complex tasks involving hand-eye coordination and rich contacts will remain an untouched vision that is only feasible in controlled lab environments. We propose dual-arm settings as platforms for robot learning. Such settings enable safe data collection for acquiring manipulation skills as well as training perception modules in a robot-supervised manner. They also ease the processes of resetting the environment. Furthermore, adversarial learning could potentially boost the generalization capability of robot learning methods by maximizing the exploration based on game-theoretic objectives while ensuring safety based on collaborative task spaces. In this paper, we will discuss the potential benefits of this setup as well as the challenges and research directions that can be pursued.