# Learning Dual-Arm Push and Grasp Synergy in Dense Clutter

arXiv: https://arxiv.org/abs/2412.04052 · HTML: https://mlsanity.com/paper/2412.04052/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 26 of its 43 references that are in the map.
- First posted on arXiv 5 Dec 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 26 of 26)

- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [6-DOF GraspNet: Variational Grasp Generation for Object Manipulation](https://mlsanity.com/paper/1905.10520/) (2019)
- [AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains](https://mlsanity.com/paper/2212.08333/) (2022)
- [Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning](https://mlsanity.com/paper/1803.09956/) (2018)
- [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)
- [Data-Driven Grasp Synthesis - A Survey](https://mlsanity.com/paper/1309.2660/) (2013)
- [Deep Learning Approaches to Grasp Synthesis: A Review](https://mlsanity.com/paper/2207.02556/) (2022)
- [Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image](https://mlsanity.com/paper/2006.05398/) (2020)
- [VL-Grasp: a 6-Dof Interactive Grasp Policy for Language-Oriented Objects in Cluttered Indoor Scenes](https://mlsanity.com/paper/2308.00640/) (2023)
- [RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images](https://mlsanity.com/paper/2103.02184/) (2021)
- [A Joint Modeling of Vision-Language-Action for Target-oriented Grasping in Clutter](https://mlsanity.com/paper/2302.12610/) (2023)
- [Goal-conditioned dual-action imitation learning for dexterous dual-arm robot manipulation](https://mlsanity.com/paper/2203.09749/) (2022)
- [Visual Foresight Trees for Object Retrieval from Clutter with Nonprehensile Rearrangement](https://mlsanity.com/paper/2105.02857/) (2021)
- [Towards Open-World Grasping with Large Vision-Language Models](https://mlsanity.com/paper/2406.18722/) (2024)
- [Occlusion-Aware Search for Object Retrieval in Clutter](https://mlsanity.com/paper/2011.03334/) (2020)
- [Efficient learning of goal-oriented push-grasping synergy in clutter](https://mlsanity.com/paper/2103.05405/) (2021)
- [Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes](https://mlsanity.com/paper/2403.18546/) (2024)
- [GE-Grasp: Efficient Target-Oriented Grasping in Dense Clutter](https://mlsanity.com/paper/2207.11941/) (2022)
- [Multi-Stage Reinforcement Learning for Non-Prehensile Manipulation](https://mlsanity.com/paper/2307.12074/) (2023)
- [Self-Supervised Learning for Joint Pushing and Grasping Policies in Highly Cluttered Environments](https://mlsanity.com/paper/2203.02511/) (2022)
- [A Coarse-to-Fine Framework for Dual-Arm Manipulation of Deformable Linear Objects with Whole-Body Obstacle Avoidance](https://mlsanity.com/paper/2209.11145/) (2022)
- [Learning Bifunctional Push-grasping Synergistic Strategy for Goal-agnostic and Goal-oriented Tasks](https://mlsanity.com/paper/2212.01763/) (2022)
- [Reinforcement Learning Based Pushing and Grasping Objects from Ungraspable Poses](https://mlsanity.com/paper/2302.13328/) (2023)
- [Toward Holistic Planning and Control Optimization for Dual-Arm Rearrangement](https://mlsanity.com/paper/2404.06758/) (2024)
- [Probabilistic Slide-support Manipulation Planning in Clutter](https://mlsanity.com/paper/2306.12649/) (2023)

## Built on by (most cited first, 1 of 1)

- [Is Single-View Mesh Reconstruction Ready for Robotics?](https://mlsanity.com/paper/2505.17966/) (2025)

## Abstract (arXiv)

Robotic grasping in densely cluttered environments is challenging due to scarce collision-free grasp affordances. Non-prehensile actions can increase feasible grasps in cluttered environments, but most research focuses on single-arm rather than dual-arm manipulation. Policies from single-arm systems fail to fully leverage the advantages of dual-arm coordination. We propose a target-oriented hierarchical deep reinforcement learning (DRL) framework that learns dual-arm push-grasp synergy for grasping objects to enhance dexterous manipulation in dense clutter. Our framework maps visual observations to actions via a pre-trained deep learning backbone and a novel CNN-based DRL model, trained with Proximal Policy Optimization (PPO), to develop a dual-arm push-grasp strategy. The backbone enhances feature mapping in densely cluttered environments. A novel fuzzy-based reward function is introduced to accelerate efficient strategy learning. Our system is developed and trained in Isaac Gym and then tested in simulations and on a real robot. Experimental results show that our framework effectively maps visual data to dual push-grasp motions, enabling the dual-arm system to grasp target objects in complex environments. Compared to other methods, our approach generates 6-DoF grasp candidates and enables dual-arm push actions, mimicking human behavior. Results show that our method efficiently completes tasks in densely cluttered environments. https://sites.google.com/view/pg4da/home