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Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods

arXiv 1802.10264 ↗First posted 28 Feb 2018cs.RO cs.LG stat.MLcited by 32

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Abstract (arXiv)

In this paper, we explore deep reinforcement learning algorithms for vision-based robotic grasping. Model-free deep reinforcement learning (RL) has been successfully applied to a range of challenging environments, but the proliferation of algorithms makes it difficult to discern which particular approach would be best suited for a rich, diverse task like grasping. To answer this question, we propose a simulated benchmark for robotic grasping that emphasizes off-policy learning and generalization to unseen objects. Off-policy learning enables utilization of grasping data over a wide variety of objects, and diversity is important to enable the method to generalize to new objects that were not seen during training. We evaluate the benchmark tasks against a variety of Q-function estimation methods, a method previously proposed for robotic grasping with deep neural network models, and a novel approach based on a combination of Monte Carlo return estimation and an off-policy correction. Our results indicate that several simple methods provide a surprisingly strong competitor to popular algorithms such as double Q-learning, and our analysis of stability sheds light on the relative tradeoffs between the algorithms.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Feb 2018.

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Citing papers per year: 2021: 6; 2022: 14; 2023: 3; 2024: 5; 2025: 0; 2026: 4.

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  1. 2022DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Videocited by 83
  2. 2021Variance-Aware Off-Policy Evaluation with Linear Function Approximationcited by 34
  3. 2022Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interactioncited by 23
  4. 2024InterACT: Inter-dependency Aware Action Chunking with Hierarchical Attention Transformers for Bimanual Manipulationcited by 21
  5. 2022An Instrumental Variable Approach to Confounded Off-Policy Evaluationcited by 20
  6. 2021PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimationcited by 14
  7. 2022Accelerating Interactive Human-like Manipulation Learning with GPU-based Simulation and High-quality Demonstrationscited by 8
  8. 2022Meta-Learning Regrasping Strategies for Physical-Agnostic Objectscited by 7
  9. 2021Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learningcited by 5
  10. 2022RMBench: Benchmarking Deep Reinforcement Learning for Robotic Manipulator Controlcited by 5
  11. 2023Model-based Offline Reinforcement Learning with Local Misspecificationcited by 5
  12. 2024Stochastic Q-learning for Large Discrete Action Spacescited by 5
20 more
  1. 2021Visionary: Vision architecture discovery for robot learningcited by 4
  2. 2021Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Taskscited by 3
  3. 2022Minimizing Human Assistance: Augmenting a Single Demonstration for Deep Reinforcement Learningcited by 3
  4. 2024Grasp Anything: Combining Teacher-Augmented Policy Gradient Learning with Instance Segmentation to Grasp Arbitrary Objectscited by 3
  5. 2022End-to-end Reinforcement Learning of Robotic Manipulation with Robust Keypoints Representationcited by 2
  6. 2022CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaborationcited by 2
  7. 2022Efficient Representations of Object Geometry for Reinforcement Learning of Interactive Grasping Policiescited by 2
  8. 2024Towards Real-World Efficiency: Domain Randomization in Reinforcement Learning for Pre-Capture of Free-Floating Moving Targets by Autonomous Robotscited by 2
  9. 2024Multi-Type Preference Learning: Empowering Preference-Based Reinforcement Learning with Equal Preferencescited by 2
  10. 2022Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Modelscited by 1
  11. 2026MSPR: Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learningcited by 1
  12. 2021Optimality Inductive Biases and Agnostic Guidelines for Offline Reinforcement Learningcited by 0
  13. 2022Benchmarking Deep Reinforcement Learning Algorithms for Vision-based Roboticscited by 0
  14. 2022GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Graspingcited by 0
  15. 2022A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Armscited by 0
  16. 2023CRC-RL: A Novel Visual Feature Representation Architecture for Unsupervised Reinforcement Learningcited by 0
  17. 2023Improving Wind Resistance Performance of Cascaded PID Controlled Quadcopters using Residual Reinforcement Learningcited by 0
  18. 2026Scalable Dexterous Robot Learning with AR-based Remote Human-Robot Interactionscited by 0
  19. 2026Learning Reach-Avoid Task with Reinforcement Learning: Vectorized Simulation and Benchmarkcited by 0
  20. 2026Iterative Grasp Pose Refinement: A Deep Reinforcement Learning Approach for 2D Visioncited by 0