# Adversarial Skill Learning for Robust Manipulation

arXiv: https://arxiv.org/abs/2011.03383 · HTML: https://mlsanity.com/paper/2011.03383/

## Key facts

- Cited by 6 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 6 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification](https://mlsanity.com/paper/2211.03413/) (2022)
- [R3L: Relative Representations for Reinforcement Learning](https://mlsanity.com/paper/2404.12917/) (2024)
- [Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation](https://mlsanity.com/paper/2310.04930/) (2023)
- [Stimulate the Potential of Robots via Competition](https://mlsanity.com/paper/2403.10487/) (2024)
- [Mapping representations in Reinforcement Learning via Semantic Alignment for Zero-Shot Stitching](https://mlsanity.com/paper/2503.01881/) (2025)
- [Robustness of Robotic Manipulation: Foundations and Frontiers](https://mlsanity.com/paper/2606.31494/) (2026)

## Abstract (arXiv)

Deep reinforcement learning has made significant progress in robotic manipulation tasks and it works well in the ideal disturbance-free environment. However, in a real-world environment, both internal and external disturbances are inevitable, thus the performance of the trained policy will dramatically drop. To improve the robustness of the policy, we introduce the adversarial training mechanism to the robotic manipulation tasks in this paper, and an adversarial skill learning algorithm based on soft actor-critic (SAC) is proposed for robust manipulation. Extensive experiments are conducted to demonstrate that the learned policy is robust to internal and external disturbances. Additionally, the proposed algorithm is evaluated in both the simulation environment and on the real robotic platform.