mlsanityData to 14 Oct 2026

Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation

arXiv 1710.06422 ↗First posted 17 Oct 2017cs.LG cs.AI cs.CVcited by 36

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Abstract (arXiv)

Learning-based approaches to robotic manipulation are limited by the scalability of data collection and accessibility of labels. In this paper, we present a multi-task domain adaptation framework for instance grasping in cluttered scenes by utilizing simulated robot experiments. Our neural network takes monocular RGB images and the instance segmentation mask of a specified target object as inputs, and predicts the probability of successfully grasping the specified object for each candidate motor command. The proposed transfer learning framework trains a model for instance grasping in simulation and uses a domain-adversarial loss to transfer the trained model to real robots using indiscriminate grasping data, which is available both in simulation and the real world. We evaluate our model in real-world robot experiments, comparing it with alternative model architectures as well as an indiscriminate grasping baseline.

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Oct 2017.

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Citing papers per year: 2020: 1; 2021: 8; 2022: 8; 2023: 8; 2024: 2; 2025: 7; 2026: 2.

36 papers.

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  1. 2021ManipulaTHOR: A Framework for Visual Object Manipulationcited by 102
  2. 2023Instance Segmentation in the Darkcited by 46
  3. 2025A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Modelscited by 38
  4. 2023Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learningcited by 36
  5. 2023A Joint Modeling of Vision-Language-Action for Target-oriented Grasping in Cluttercited by 34
  6. 2021INVIGORATE: Interactive Visual Grounding and Grasping in Cluttercited by 32
  7. 2023Polybot: Training One Policy Across Robots While Embracing Variabilitycited by 28
  8. 2022Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interactioncited by 24
  9. 2021Collision-Aware Target-Driven Object Grasping in Constrained Environmentscited by 11
  10. 2021REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Cluttercited by 11
  11. 2021Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillationcited by 9
  12. 2022Self-Supervised Interactive Object Segmentation Through a Singulation-and-Grasping Approachcited by 8
24 more
  1. 2025Attribute-Based Robotic Grasping with Data-Efficient Adaptationcited by 8
  2. 2020Domain-Adversarial and Conditional State Space Model for Imitation Learningcited by 7
  3. 2021Attribute-Based Robotic Grasping with One-Grasp Adaptationcited by 7
  4. 2022Practical Imitation Learning in the Real World via Task Consistency Losscited by 7
  5. 2023DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Controlcited by 7
  6. 2022PI-QT-Opt: Predictive Information Improves Multi-Task Robotic Reinforcement Learning at Scalecited by 5
  7. 2024Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptationcited by 4
  8. 2024Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantificationcited by 4
  9. 2025ADPro: a Test-time Adaptive Diffusion Policy via Manifold-constrained Denoising and Task-aware Initialization for Robotic Manipulationcited by 4
  10. 2025Invariance Co-training for Robot Visual Generalizationcited by 4
  11. 2021SIM2REALVIZ: Visualizing the Sim2Real Gap in Robot Ego-Pose Estimationcited by 3
  12. 2022SafePicking: Learning Safe Object Extraction via Object-Level Mappingcited by 3
  13. 2022One-Shot Domain Adaptive and Generalizable Semantic Segmentation with Class-Aware Cross-Domain Transformerscited by 3
  14. 2023Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Controlcited by 3
  15. 2025Efficient Alignment of Unconditioned Action Prior for Language-conditioned Pick and Place in Cluttercited by 3
  16. 2022CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaborationcited by 2
  17. 2021On the Challenges of Open World Recognitionunder Shifting Visual Domainscited by 1
  18. 2025SAGA: Open-World Mobile Manipulation via Structured Affordance Groundingcited by 1
  19. 2022Autoencoder for Synthetic to Real Generalization: From Simple to More Complex Scenescited by 0
  20. 2023QwenGrasp: A Usage of Large Vision-Language Model for Target-Oriented Graspingcited by 0
  21. 2023SKU-Patch: Towards Efficient Instance Segmentation for Unseen Objects in Auto-Storecited by 0
  22. 2025Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Controlcited by 0
  23. 2026Developing AI Agents with Simulated Data: Why, what, and how?cited by 0
  24. 2026Hybrid supervised instance segmentation by learning label noise suppressioncited by 0