# Supervision via Competition: Robot Adversaries for Learning Tasks

arXiv: https://arxiv.org/abs/1610.01685 · HTML: https://mlsanity.com/paper/1610.01685/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2016.

## Built on by (most cited first, 16 of 16)

- [Evolving Curricula with Regret-Based Environment Design](https://mlsanity.com/paper/2203.01302/) (2022)
- [Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage](https://mlsanity.com/paper/2305.09659/) (2023)
- [CLUTR: Curriculum Learning via Unsupervised Task Representation Learning](https://mlsanity.com/paper/2210.10243/) (2022)
- [Stabilizing Unsupervised Environment Design with a Learned Adversary](https://mlsanity.com/paper/2308.10797/) (2023)
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- [Real-time Holistic Robot Pose Estimation with Unknown States](https://mlsanity.com/paper/2402.05655/) (2024)
- [Robust Value Iteration for Continuous Control Tasks](https://mlsanity.com/paper/2105.12189/) (2021)
- [Continuous-Time Fitted Value Iteration for Robust Policies](https://mlsanity.com/paper/2110.01954/) (2021)
- [Bridging the Gap between Artificial Intelligence and Artificial General Intelligence: A Ten Commandment Framework for Human-Like Intelligence](https://mlsanity.com/paper/2210.09366/) (2022)
- [Adversarial Style Transfer for Robust Policy Optimization in Deep Reinforcement Learning](https://mlsanity.com/paper/2308.15550/) (2023)
- [Training Robots to Evaluate Robots: Example-Based Interactive Reward Functions for Policy Learning](https://mlsanity.com/paper/2212.08961/) (2022)
- [COvolve: Adversarial Co-Evolution of Large-Language-Model-Generated Policies and Environments via Two-Player Zero-Sum Game](https://mlsanity.com/paper/2603.28386/) (2026)
- [Boosting Exploration in Multi-Task Reinforcement Learning using Adversarial Networks](https://mlsanity.com/paper/2201.11783/) (2022)
- [Close-Proximity Satellite Operations through Deep Reinforcement Learning and Terrestrial Testing Environments](https://mlsanity.com/paper/2502.20554/) (2025)
- [Robustness of Robotic Manipulation: Foundations and Frontiers](https://mlsanity.com/paper/2606.31494/) (2026)

## Abstract (arXiv)

There has been a recent paradigm shift in robotics to data-driven learning for planning and control. Due to large number of experiences required for training, most of these approaches use a self-supervised paradigm: using sensors to measure success/failure. However, in most cases, these sensors provide weak supervision at best. In this work, we propose an adversarial learning framework that pits an adversary against the robot learning the task. In an effort to defeat the adversary, the original robot learns to perform the task with more robustness leading to overall improved performance. We show that this adversarial framework forces the the robot to learn a better grasping model in order to overcome the adversary. By grasping 82% of presented novel objects compared to 68% without an adversary, we demonstrate the utility of creating adversaries. We also demonstrate via experiments that having robots in adversarial setting might be a better learning strategy as compared to having collaborative multiple robots.