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IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data

arXiv 1911.05321 ↗First posted 13 Nov 2019cs.RO cs.AI cs.LGcited by 85

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Cited by 54 in Offline RL and imitation learning from data, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Learning from offline task demonstrations is a problem of great interest in robotics. For simple short-horizon manipulation tasks with modest variation in task instances, offline learning from a small set of demonstrations can produce controllers that successfully solve the task. However, leveraging a fixed batch of data can be problematic for larger datasets and longer-horizon tasks with greater variations. The data can exhibit substantial diversity and consist of suboptimal solution approaches. In this paper, we propose Implicit Reinforcement without Interaction at Scale (IRIS), a novel framework for learning from large-scale demonstration datasets. IRIS factorizes the control problem into a goal-conditioned low-level controller that imitates short demonstration sequences and a high-level goal selection mechanism that sets goals for the low-level and selectively combines parts of suboptimal solutions leading to more successful task completions. We evaluate IRIS across three datasets, including the RoboTurk Cans dataset collected by humans via crowdsourcing, and show that performant policies can be learned from purely offline learning. Additional results at https://sites.google.com/stanford.edu/iris/ .

Key facts. Cited by 85 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Nov 2019.

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Citing papers per year: 2020: 1; 2021: 11; 2022: 22; 2023: 15; 2024: 18; 2025: 13; 2026: 5.

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  1. 2021What Matters in Learning from Offline Human Demonstrations for Robot Manipulationcited by 699
  2. 2021COMBO: Conservative Offline Model-Based Policy Optimizationcited by 365
  3. 2023MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrationscited by 308
  4. 2025π*0.6: a VLA That Learns From Experiencecited by 280
  5. 20243D Diffuser Actor: Policy Diffusion with 3D Scene Representationscited by 278
  6. 2023MimicPlay: Long-Horizon Imitation Learning by Watching Human Playcited by 214
  7. 2022VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priorscited by 135
  8. 2022RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learningcited by 115
  9. 2022Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deploymentcited by 101
  10. 2021Learning by Watching: Physical Imitation of Manipulation Skills from Human Videoscited by 87
  11. 2022When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?cited by 86
  12. 2021S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learningcited by 85
38 more
  1. 2023Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidancecited by 85
  2. 2020COG: Connecting New Skills to Past Experience with Offline Reinforcement Learningcited by 83
  3. 2021Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulationcited by 82
  4. 2022BITS: Bi-level Imitation for Traffic Simulationcited by 80
  5. 2022Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trialscited by 78
  6. 2022Learning and Retrieval from Prior Data for Skill-based Imitation Learningcited by 77
  7. 2021Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Taskscited by 75
  8. 2023XSkill: Cross Embodiment Skill Discoverycited by 74
  9. 2024Simple Hierarchical Planning with Diffusioncited by 71
  10. 2021A Workflow for Offline Model-Free Robotic Reinforcement Learningcited by 68
  11. 2021Conservative Data Sharing for Multi-Task Offline Reinforcement Learningcited by 64
  12. 2023LOTUS: Continual Imitation Learning for Robot Manipulation Through Unsupervised Skill Discoverycited by 63
  13. 2022Efficient Planning in a Compact Latent Action Spacecited by 57
  14. 2025Horizon Reduction Makes RL Scalablecited by 55
  15. 2022Imitation Learning by Estimating Expertise of Demonstratorscited by 50
  16. 2022Skill-based Model-based Reinforcement Learningcited by 50
  17. 2022Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learningcited by 48
  18. 2024Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Samplingcited by 46
  19. 2022Hierarchical Model-Based Imitation Learning for Planning in Autonomous Drivingcited by 43
  20. 2021Offline Meta-Reinforcement Learning for Industrial Insertioncited by 37
  21. 2023AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agentscited by 36
  22. 2024Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrationscited by 35
  23. 2022Chain of Thought Imitation with Procedure Cloningcited by 30
  24. 2022Offline Reinforcement Learning for Visual Navigationcited by 28
  25. 2023Human-in-the-Loop Task and Motion Planning for Imitation Learningcited by 28
  26. 2022Goal-conditioned dual-action imitation learning for dexterous dual-arm robot manipulationcited by 21
  27. 2022Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imaginationcited by 21
  28. 2022PATO: Policy Assisted TeleOperation for Scalable Robot Data Collectioncited by 19
  29. 2021Guided Imitation of Task and Motion Planningcited by 18
  30. 2025Dexterous Manipulation through Imitation Learning: A Surveycited by 18
  31. 2022Hierarchical Decision Transformercited by 16
  32. 2024Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RLcited by 15
  33. 2023IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Controlcited by 14
  34. 2024GHIL-Glue: Hierarchical Control with Filtered Subgoal Imagescited by 14
  35. 2023Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skillscited by 13
  36. 2025IMLE Policy: Fast and Sample Efficient Visuomotor Policy Learning via Implicit Maximum Likelihood Estimationcited by 13
  37. 2025FlowRAM: Grounding Flow Matching Policy with Region-Aware Mamba Framework for Robotic Manipulationcited by 13
  38. 2022Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Datacited by 12

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