# GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning

arXiv: https://arxiv.org/abs/2203.02119 · HTML: https://mlsanity.com/paper/2203.02119/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 4 Mar 2022.

## Builds on (most cited first, 19 of 19)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation](https://mlsanity.com/paper/1806.10293/) (2018)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion](https://mlsanity.com/paper/1901.04780/) (2019)
- [Adversarial Attacks on Neural Network Policies](https://mlsanity.com/paper/1702.02284/) (2017)
- [6-DOF GraspNet: Variational Grasp Generation for Object Manipulation](https://mlsanity.com/paper/1905.10520/) (2019)
- [Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects](https://mlsanity.com/paper/1809.10790/) (2018)
- [Emergent Complexity via Multi-Agent Competition](https://mlsanity.com/paper/1710.03748/) (2017)
- [Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach](https://mlsanity.com/paper/1804.05172/) (2018)
- [Grasping in the Wild:Learning 6DoF Closed-Loop Grasping from Low-Cost Demonstrations](https://mlsanity.com/paper/1912.04344/) (2019)
- [Object-Centric Task and Motion Planning in Dynamic Environments](https://mlsanity.com/paper/1911.04679/) (2019)
- [Learning a visuomotor controller for real world robotic grasping using simulated depth images](https://mlsanity.com/paper/1706.04652/) (2017)
- [Depth-Based Object Tracking Using a Robust Gaussian Filter](https://mlsanity.com/paper/1602.06157/) (2016)
- [Dynamic Grasping with Reachability and Motion Awareness](https://mlsanity.com/paper/2103.10562/) (2021)
- [Provably Constant-time Planning and Replanning for Real-time Grasping Objects off a Conveyor Belt](https://mlsanity.com/paper/2101.07148/) (2021)
- [Learning Agile Locomotion via Adversarial Training](https://mlsanity.com/paper/2008.00603/) (2020)
- [Reaching, Grasping and Re-grasping: Learning Multimode Grasping Skills](https://mlsanity.com/paper/2002.04498/) (2020)
- [Dual Quaternion-Based Visual Servoing for Grasping Moving Objects](https://mlsanity.com/paper/2107.08149/) (2021)
- [Multi-Task Reinforcement Learning based Mobile Manipulation Control for Dynamic Object Tracking and Grasping](https://mlsanity.com/paper/2006.04271/) (2020)

## Built on by (most cited first, 8 of 8)

- [Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy](https://mlsanity.com/paper/2407.06813/) (2024)
- [GraspGF: Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping](https://mlsanity.com/paper/2309.06038/) (2023)
- [Forecasting Continuous Non-Conservative Dynamical Systems in SO(3)](https://mlsanity.com/paper/2508.07775/) (2025)
- [Continuous-Time SO(3) Forecasting with Savitzky--Golay Neural Controlled Differential Equations](https://mlsanity.com/paper/2506.06780/) (2025)
- [Object Manipulation in Marine Environments using Reinforcement Learning](https://mlsanity.com/paper/2406.03223/) (2024)
- [The Role of Tactile Sensing for Learning Reach and Grasp](https://mlsanity.com/paper/2502.20367/) (2025)
- [SpikeGrasp: A Benchmark for 6-DoF Grasp Pose Detection from Stereo Spike Streams](https://mlsanity.com/paper/2510.10602/) (2025)
- [DynaWM: A Base-VLA-Guided World Foundation Model for Moving-Object Manipulation](https://mlsanity.com/paper/2607.02604/) (2026)

## Abstract (arXiv)

Grasping moving objects, such as goods on a belt or living animals, is an important but challenging task in robotics. Conventional approaches rely on a set of manually defined object motion patterns for training, resulting in poor generalization to unseen object trajectories. In this work, we introduce an adversarial reinforcement learning framework for dynamic grasping, namely GraspARL. To be specific. we formulate the dynamic grasping problem as a 'move-and-grasp' game, where the robot is to pick up the object on the mover and the adversarial mover is to find a path to escape it. Hence, the two agents play a min-max game and are trained by reinforcement learning. In this way, the mover can auto-generate diverse moving trajectories while training. And the robot trained with the adversarial trajectories can generalize to various motion patterns. Empirical results on the simulator and real-world scenario demonstrate the effectiveness of each and good generalization of our method.