mlsanityData to 14 Oct 2026

Pseudo-Labeling and Contextual Curriculum Learning for Online Grasp Learning in Robotic Bin Picking

arXiv 2403.02495 ↗First posted 4 Mar 2024cs.RO cs.AIcited by 1

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Abstract (arXiv)

The prevailing grasp prediction methods predominantly rely on offline learning, overlooking the dynamic grasp learning that occurs during real-time adaptation to novel picking scenarios. These scenarios may involve previously unseen objects, variations in camera perspectives, and bin configurations, among other factors. In this paper, we introduce a novel approach, SSL-ConvSAC, that combines semi-supervised learning and reinforcement learning for online grasp learning. By treating pixels with reward feedback as labeled data and others as unlabeled, it efficiently exploits unlabeled data to enhance learning. In addition, we address the imbalance between labeled and unlabeled data by proposing a contextual curriculum-based method. We ablate the proposed approach on real-world evaluation data and demonstrate promise for improving online grasp learning on bin picking tasks using a physical 7-DoF Franka Emika robot arm with a suction gripper. Video: https://youtu.be/OAro5pg8I9U

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 31 of its 42 references that are in the map. First posted on arXiv 4 Mar 2024.

Builds on

31 of 42 references.

References · 31

  1. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  2. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 743
  3. 2021FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labelingcited by 540
  4. 2019Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learningcited by 510
  5. 2016Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collectioncited by 316
  6. 2021End-to-End Semi-Supervised Object Detection with Soft Teachercited by 316
  7. 2020A Simple Semi-Supervised Learning Framework for Object Detectioncited by 282
  8. 2022FreeMatch: Self-adaptive Thresholding for Semi-supervised Learningcited by 232
  9. 2021Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenescited by 218
  10. 2023SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learningcited by 133
  11. 2018Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learningcited by 128
  12. 2017Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matchingcited by 125
19 more
  1. 2018Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approachcited by 120
  2. 2013Data-Driven Grasp Synthesis - A Surveycited by 106
  3. 2022Deep Learning Approaches to Grasp Synthesis: A Reviewcited by 72
  4. 2022Sample Efficient Grasp Learning Using Equivariant Modelscited by 53
  5. 2022PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detectioncited by 51
  6. 2021SuctionNet-1Billion: A Large-Scale Benchmark for Suction Graspingcited by 32
  7. 2020Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detectioncited by 29
  8. 2023HACMan: Learning Hybrid Actor-Critic Maps for 6D Non-Prehensile Manipulationcited by 29
  9. 2019Robot Learning of Shifting Objects for Grasping in Cluttered Environmentscited by 13
  10. 2022DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detectioncited by 12
  11. 2020Semi-Supervised Object Detection with Sparsely Annotated Datasetcited by 10
  12. 2023Calibrated Teacher for Sparsely Annotated Object Detectioncited by 10
  13. 2020Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objectscited by 9
  14. 2021Robot Learning of 6 DoF Grasping using Model-based Adaptive Primitivescited by 7
  15. 2021A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitivescited by 6
  16. 2023Uncertainty-driven Exploration Strategies for Online Grasp Learningcited by 6
  17. 2021LEGS: Learning Efficient Grasp Sets for Exploratory Graspingcited by 5
  18. 2023Model-free Grasping with Multi-Suction Cup Grippers for Robotic Bin Pickingcited by 5
  19. 2020Accelerating Grasp Exploration by Leveraging Learned Priorscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Continual Learning for 6-DoF Grasp Synthesis via Experience and Demonstrationscited by 0

Similar, not cited

Close in wording, no citation either way

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  2. 2024GAP-RL: Grasps As Points for RL Towards Dynamic Object Grasping ↗
  3. 2024Self-Supervised Learning For Robust Robotic Grasping In Dynamic Environment ↗
  4. 2025Universal Dexterous Functional Grasping via Demonstration-Editing Reinforcement Learning
  5. 2024Offline-to-online Reinforcement Learning for Image-based Grasping with Scarce Demonstrations
  6. 2025Dexterous Grasping with Real-World Robotic Reinforcement Learning
  7. 2025DemoGrasp: Universal Dexterous Grasping from a Single Demonstration
  8. 2026Iterative Grasp Pose Refinement: A Deep Reinforcement Learning Approach for 2D Vision