# Robotic Grasping using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2007.04499 · HTML: https://mlsanity.com/paper/2007.04499/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2020.

## Built on by (most cited first, 28 of 28)

- [HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model](https://mlsanity.com/paper/2503.10631/) (2025)
- [RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation](https://mlsanity.com/paper/2412.13877/) (2024)
- [ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2312.16217/) (2023)
- [RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation](https://mlsanity.com/paper/2406.04339/) (2024)
- [Lift3D Foundation Policy: Lifting 2D Large-Scale Pretrained Models for Robust 3D Robotic Manipulation](https://mlsanity.com/paper/2411.18623/) (2024)
- [CoMo: Learning Continuous Latent Motion from Internet Videos for Scalable Robot Learning](https://mlsanity.com/paper/2505.17006/) (2025)
- [AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation](https://mlsanity.com/paper/2507.01961/) (2025)
- [A Self-Correcting Vision-Language-Action Model for Fast and Slow System Manipulation](https://mlsanity.com/paper/2405.17418/) (2024)
- [From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots](https://mlsanity.com/paper/2506.12779/) (2025)
- [SG-VLA: Learning Spatially-Grounded Vision-Language-Action Models for Mobile Manipulation](https://mlsanity.com/paper/2603.22760/) (2026)
- [ImageManip: Image-based Robotic Manipulation with Affordance-guided Next View Selection](https://mlsanity.com/paper/2310.09069/) (2023)
- [World Robot Challenge 2020 -- Partner Robot: A Data-Driven Approach for Room Tidying with Mobile Manipulator](https://mlsanity.com/paper/2207.10106/) (2022)
- [A Survey of Reinforcement Learning for Optimization in Automation](https://mlsanity.com/paper/2502.09417/) (2025)
- [Grasping Deformable Objects via Reinforcement Learning with Cross-Modal Attention to Visuo-Tactile Inputs](https://mlsanity.com/paper/2504.15595/) (2025)
- [Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning](https://mlsanity.com/paper/2307.16062/) (2023)
- [End-to-end Reinforcement Learning of Robotic Manipulation with Robust Keypoints Representation](https://mlsanity.com/paper/2202.06027/) (2022)
- [Efficient Representations of Object Geometry for Reinforcement Learning of Interactive Grasping Policies](https://mlsanity.com/paper/2211.10957/) (2022)
- [Towards Real-World Efficiency: Domain Randomization in Reinforcement Learning for Pre-Capture of Free-Floating Moving Targets by Autonomous Robots](https://mlsanity.com/paper/2406.06460/) (2024)
- [Bayesian optimization for robust robotic grasping using a sensorized compliant hand](https://mlsanity.com/paper/2410.18237/) (2024)
- [State Representations as Incentives for Reinforcement Learning Agents: A Sim2Real Analysis on Robotic Grasping](https://mlsanity.com/paper/2309.11984/) (2023)
- [Benchmarking Deep Reinforcement Learning Algorithms for Vision-based Robotics](https://mlsanity.com/paper/2201.04224/) (2022)
- [GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping](https://mlsanity.com/paper/2204.06835/) (2022)
- [Enhancing team performance with transfer-learning during real-world human-robot collaboration](https://mlsanity.com/paper/2211.13070/) (2022)
- [Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement Learning](https://mlsanity.com/paper/2301.13343/) (2023)
- [CRC-RL: A Novel Visual Feature Representation Architecture for Unsupervised Reinforcement Learning](https://mlsanity.com/paper/2301.13473/) (2023)
- [Toward Deployable Multi-Robot Collaboration via a Symbolically-Guided Decision Transformer](https://mlsanity.com/paper/2508.13877/) (2025)
- [DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation](https://mlsanity.com/paper/2509.04970/) (2025)
- [Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review](https://mlsanity.com/paper/2510.21758/) (2025)

## Abstract (arXiv)

In this work, we present a deep reinforcement learning based method to solve the problem of robotic grasping using visio-motor feedback. The use of a deep learning based approach reduces the complexity caused by the use of hand-designed features. Our method uses an off-policy reinforcement learning framework to learn the grasping policy. We use the double deep Q-learning framework along with a novel Grasp-Q-Network to output grasp probabilities used to learn grasps that maximize the pick success. We propose a visual servoing mechanism that uses a multi-view camera setup that observes the scene which contains the objects of interest. We performed experiments using a Baxter Gazebo simulated environment as well as on the actual robot. The results show that our proposed method outperforms the baseline Q-learning framework and increases grasping accuracy by adapting a multi-view model in comparison to a single-view model.