mlsanityData to 7 Oct 2026

HG-DAgger: Interactive Imitation Learning with Human Experts

arXiv 1810.02890 ↗First posted 5 Oct 2018cs.ROcited by 245

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

Imitation learning has proven to be useful for many real-world problems, but approaches such as behavioral cloning suffer from data mismatch and compounding error issues. One attempt to address these limitations is the DAgger algorithm, which uses the state distribution induced by the novice to sample corrective actions from the expert. Such sampling schemes, however, require the expert to provide action labels without being fully in control of the system. This can decrease safety and, when using humans as experts, is likely to degrade the quality of the collected labels due to perceived actuator lag. In this work, we propose HG-DAgger, a variant of DAgger that is more suitable for interactive imitation learning from human experts in real-world systems. In addition to training a novice policy, HG-DAgger also learns a safety threshold for a model-uncertainty-based risk metric that can be used to predict the performance of the fully trained novice in different regions of the state space. We evaluate our method on both a simulated and real-world autonomous driving task, and demonstrate improved performance over both DAgger and behavioral cloning.

Key facts. Cited by 245 papers in the map (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 9 of its 31 references that are in the map. First posted on arXiv 5 Oct 2018.

Builds on

9 of 31 references.

Closest ancestors · 1

Same direction, newest first

  1. 2018EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning22 Jul

Other references · 8

  1. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  2. 2016End to End Learning for Self-Driving Carscited by 1,000
  3. 2018Neural Processescited by 307
  4. 2017Trial without Error: Towards Safe Reinforcement Learning via Human Interventioncited by 102
  5. 2016Query-Efficient Imitation Learning for End-to-End Autonomous Drivingcited by 83
  6. 2014Interactive Policy Learning through Confidence-Based Autonomycited by 29
  7. 2016Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrationscited by 17
  8. 2017Agile Autonomous Driving using End-to-End Deep Imitation Learningcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 2; 2021: 11; 2022: 23; 2023: 32; 2024: 39; 2025: 52; 2026: 86.

245 papers.

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  1. 2023Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardwarecited by 1,490
  2. 2023On-Policy Distillation of Language Models: Learning from Self-Generated Mistakescited by 840
  3. 2022BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learningcited by 517
  4. 2024Robotic Control via Embodied Chain-of-Thought Reasoningcited by 340
  5. 2025π*0.6: a VLA That Learns From Experiencecited by 280
  6. 2022Interactive Language: Talking to Robots in Real Timecited by 217
  7. 2024RT-H: Action Hierarchies Using Languagecited by 204
  8. 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challengescited by 145
  9. 2025ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policycited by 139
  10. 2020Human-in-the-Loop Imitation Learning using Remote Teleoperationcited by 103
  11. 2022Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deploymentcited by 101
  12. 2023UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learningcited by 101
38 more
  1. 2021ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learningcited by 98
  2. 2024AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agentscited by 83
  3. 2022Learning and Retrieval from Prior Data for Skill-based Imitation Learningcited by 77
  4. 2023Data Quality in Imitation Learningcited by 72
  5. 2025Self-Improving Vision-Language-Action Models with Data Generation via Residual RLcited by 72
  6. 2023Imitating Task and Motion Planning with Visuomotor Transformerscited by 63
  7. 2024Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progresscited by 63
  8. 2023"No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomycited by 60
  9. 2024Real-World Robot Applications of Foundation Models: A Reviewcited by 60
  10. 2023The Wisdom of Hindsight Makes Language Models Better Instruction Followerscited by 59
  11. 2023Multi-Stage Cable Routing through Hierarchical Imitation Learningcited by 59
  12. 2023NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesiscited by 56
  13. 2023Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasetscited by 56
  14. 2023HYDRA: Hybrid Robot Actions for Imitation Learningcited by 54
  15. 2021LazyDAgger: Reducing Context Switching in Interactive Imitation Learningcited by 50
  16. 2024TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correctioncited by 50
  17. 2022Learning Generalizable Dexterous Manipulation from Human Grasp Affordancecited by 46
  18. 2024Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Samplingcited by 46
  19. 2022Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimizationcited by 44
  20. 2026RL Token: Bootstrapping Online RL with Vision-Language-Action Modelscited by 43
  21. 2025Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon Robotic Manipulationcited by 40
  22. 2023Model-Based Runtime Monitoring with Interactive Imitation Learningcited by 37
  23. 2024Efficient Data Collection for Robotic Manipulation via Compositional Generalizationcited by 35
  24. 2025RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correctioncited by 35
  25. 2023Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behaviorcited by 33
  26. 2024Multi-Task Interactive Robot Fleet Learning with Visual World Modelscited by 33
  27. 2024HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Drivingcited by 31
  28. 2021Safe Driving via Expert Guided Policy Optimizationcited by 29
  29. 2022Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervisioncited by 29
  30. 2024Diff-DAgger: Uncertainty Estimation with Diffusion Policy for Robotic Manipulationcited by 29
  31. 2023Human-in-the-Loop Task and Motion Planning for Imitation Learningcited by 28
  32. 2023Interactive Robot Learning from Verbal Correctioncited by 28
  33. 2025LLM-Based Human-Agent Collaboration and Interaction Systems: A Surveycited by 28
  34. 2025Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Correctionscited by 25
  35. 2023RLIF: Interactive Imitation Learning as Reinforcement Learningcited by 24
  36. 2024IntervenGen: Interventional Data Generation for Robust and Data-Efficient Robot Imitation Learningcited by 24
  37. 2025SRT-H: A Hierarchical Framework for Autonomous Surgery via Language Conditioned Imitation Learningcited by 23
  38. 2026GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learningcited by 23

50 most cited of 245 All 245 (JSON)

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  1. 2024Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning
  2. 2026Reward-DAgger: Robot-Gated Interactive Imitation Learning with General-Purpose Progress-Based Reward Models
  3. 2021Imitation Learning: Progress, Taxonomies and Challenges
  4. 2026WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models
  5. 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios
  6. 2025A Model-Based Approach to Imitation Learning through Multi-Step Predictions
  7. 2025Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advances
  8. 2024Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning