mlsanityData to 7 Oct 2026

Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets

arXiv 2109.13396 ↗First posted 27 Sep 2021cs.RO cs.AIcited by 248

Not on the map. Why?

Cited by 78 in Vision-language-action models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Robot learning holds the promise of learning policies that generalize broadly. However, such generalization requires sufficiently diverse datasets of the task of interest, which can be prohibitively expensive to collect. In other fields, such as computer vision, it is common to utilize shared, reusable datasets, such as ImageNet, to overcome this challenge, but this has proven difficult in robotics. In this paper, we ask: what would it take to enable practical data reuse in robotics for end-to-end skill learning? We hypothesize that the key is to use datasets with multiple tasks and multiple domains, such that a new user that wants to train their robot to perform a new task in a new domain can include this dataset in their training process and benefit from cross-task and cross-domain generalization. To evaluate this hypothesis, we collect a large multi-domain and multi-task dataset, with 7,200 demonstrations constituting 71 tasks across 10 environments, and empirically study how this data can improve the learning of new tasks in new environments. We find that jointly training with the proposed dataset and 50 demonstrations of a never-before-seen task in a new domain on average leads to a 2x improvement in success rate compared to using target domain data alone. We also find that data for only a few tasks in a new domain can bridge the domain gap and make it possible for a robot to perform a variety of prior tasks that were only seen in other domains. These results suggest that reusing diverse multi-task and multi-domain datasets, including our open-source dataset, may pave the way for broader robot generalization, eliminating the need to re-collect data for each new robot learning project.

Key facts. Cited by 248 papers in the map (data through 7 Oct 2026). Builds on 25 of its 32 references that are in the map. First posted on arXiv 27 Sep 2021.

Builds on

25 of 32 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2016Generative Adversarial Imitation Learningcited by 1,576
  4. 2020Gradient Surgery for Multi-Task Learningcited by 1,210
  5. 2015End-to-End Training of Deep Visuomotor Policiescited by 847
  6. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 730
  7. 2017Time-Contrastive Networks: Self-Supervised Learning from Videocited by 453
  8. 2016Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collectioncited by 312
  9. 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 286
  10. 2016Deep Visual Foresight for Planning Robot Motioncited by 276
  11. 2019RoboNet: Large-Scale Multi-Robot Learningcited by 268
  12. 2017One-Shot Imitation Learningcited by 232
13 more
  1. 2021MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scalecited by 226
  2. 2017One-Shot Visual Imitation Learning via Meta-Learningcited by 225
  3. 2018Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Controlcited by 220
  4. 2015Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hourscited by 208
  5. 2018RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitationcited by 202
  6. 2017Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translationcited by 167
  7. 2015Deep Spatial Autoencoders for Visuomotor Learningcited by 149
  8. 2018One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learningcited by 146
  9. 2019TossingBot: Learning to Throw Arbitrary Objects with Residual Physicscited by 131
  10. 2020Visual Imitation Made Easycited by 111
  11. 2018Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitationcited by 76
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 20; 2023: 43; 2024: 59; 2025: 85; 2026: 41.

248 papers.

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  1. 2024OpenVLA: An Open-Source Vision-Language-Action Modelcited by 2,777
  2. 2024π0: A Vision-Language-Action Flow Model for General Robot Controlcited by 2,297
  3. 2022RT-1: Robotics Transformer for Real-World Control at Scalecited by 2,007
  4. 2025π0.5: a Vision-Language-Action Model with Open-World Generalizationcited by 1,653
  5. 2023Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardwarecited by 1,490
  6. 2024Octo: An Open-Source Generalist Robot Policycited by 1,427
  7. 2025GR00T N1: An Open Foundation Model for Generalist Humanoid Robotscited by 1,167
  8. 2024WorldSimBench: Towards Video Generation Models as World Simulatorscited by 867
  9. 2022R3M: A Universal Visual Representation for Robot Manipulationcited by 678
  10. 2023BridgeData V2: A Dataset for Robot Learning at Scalecited by 634
  11. 2025SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Roboticscited by 507
  12. 2023Learning Universal Policies via Text-Guided Video Generationcited by 506
38 more
  1. 2025SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Modelcited by 502
  2. 2025RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulationcited by 477
  3. 2023Learning Interactive Real-World Simulatorscited by 440
  4. 2025CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Modelscited by 440
  5. 2024CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulationcited by 411
  6. 2024RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robotscited by 391
  7. 2025AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systemscited by 390
  8. 2022VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Trainingcited by 384
  9. 2024Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperationcited by 377
  10. 2022DayDreamer: World Models for Physical Robot Learningcited by 372
  11. 2024Robotic Control via Embodied Chain-of-Thought Reasoningcited by 340
  12. 2024TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulationcited by 331
  13. 2023MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrationscited by 308
  14. 2023Language to Rewards for Robotic Skill Synthesiscited by 307
  15. 2024TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policiescited by 298
  16. 2024Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representationscited by 279
  17. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 274
  18. 2023Affordances from Human Videos as a Versatile Representation for Roboticscited by 221
  19. 2023A Survey on Video Diffusion Modelscited by 203
  20. 2025DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledgecited by 203
  21. 2025HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Modelcited by 176
  22. 2023Scaling Robot Learning with Semantically Imagined Experiencecited by 175
  23. 2023RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shotcited by 173
  24. 2023RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunkingcited by 167
  25. 2024RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulationcited by 166
  26. 2025VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learningcited by 166
  27. 2023Foundation Models for Decision Making: Problems, Methods, and Opportunitiescited by 160
  28. 2023Video Language Planningcited by 160
  29. 2023Learning to Act from Actionless Videos through Dense Correspondencescited by 159
  30. 2023Structured World Models from Human Videoscited by 144
  31. 2024Data Scaling Laws in Imitation Learning for Robotic Manipulationcited by 144
  32. 2025Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Bettercited by 142
  33. 2023RoboCLIP: One Demonstration is Enough to Learn Robot Policiescited by 128
  34. 2024THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulationcited by 128
  35. 2024Flow as the Cross-Domain Manipulation Interfacecited by 126
  36. 2025NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Taskscited by 119
  37. 2025RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twinscited by 117
  38. 2022Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scalecited by 116

50 most cited of 248 All 248 (JSON)