# Self-Supervised Learning for Joint Pushing and Grasping Policies in Highly Cluttered Environments

arXiv: https://arxiv.org/abs/2203.02511 · HTML: https://mlsanity.com/paper/2203.02511/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2022.

## Built on by (most cited first, 5 of 5)

- [PreAfford: Universal Affordance-Based Pre-Grasping for Diverse Objects and Environments](https://mlsanity.com/paper/2404.03634/) (2024)
- [Learning Dual-Arm Push and Grasp Synergy in Dense Clutter](https://mlsanity.com/paper/2412.04052/) (2024)
- [Learning to Push, Group, and Grasp: A Diffusion Policy Approach for Multi-Object Delivery](https://mlsanity.com/paper/2502.08452/) (2025)
- [Learning Dual-Arm Coordination for Grasping Large Flat Objects](https://mlsanity.com/paper/2504.03500/) (2025)
- [Learning Object-Centric Spatial Reasoning for Sequential Manipulation in Cluttered Environments](https://mlsanity.com/paper/2603.02511/) (2026)

## Abstract (arXiv)

Robots often face situations where grasping a goal object is desirable but not feasible due to other present objects preventing the grasp action. We present a deep Reinforcement Learning approach to learn grasping and pushing policies for manipulating a goal object in highly cluttered environments to address this problem. In particular, a dual Reinforcement Learning model approach is proposed, which presents high resilience in handling complicated scenes, reaching an average of 98% task completion using primitive objects in a simulation environment. To evaluate the performance of the proposed approach, we performed two extensive sets of experiments in packed objects and a pile of object scenarios with a total of 1000 test runs in simulation. Experimental results showed that the proposed method worked very well in both scenarios and outperformed the recent state-of-the-art approaches. Demo video, trained models, and source code for the results reproducibility purpose are publicly available. https://sites.google.com/view/pushandgrasp/home