mlsanityData to 14 Oct 2026

Query-Efficient Imitation Learning for End-to-End Autonomous Driving

arXiv 1605.06450 ↗First posted 20 May 2016cs.LG cs.AI cs.ROcited by 84

Not on the map. Why?

Cited by 49 in Offline RL and imitation learning from data, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

One way to approach end-to-end autonomous driving is to learn a policy function that maps from a sensory input, such as an image frame from a front-facing camera, to a driving action, by imitating an expert driver, or a reference policy. This can be done by supervised learning, where a policy function is tuned to minimize the difference between the predicted and ground-truth actions. A policy function trained in this way however is known to suffer from unexpected behaviours due to the mismatch between the states reachable by the reference policy and trained policy functions. More advanced algorithms for imitation learning, such as DAgger, addresses this issue by iteratively collecting training examples from both reference and trained policies. These algorithms often requires a large number of queries to a reference policy, which is undesirable as the reference policy is often expensive. In this paper, we propose an extension of the DAgger, called SafeDAgger, that is query-efficient and more suitable for end-to-end autonomous driving. We evaluate the proposed SafeDAgger in a car racing simulator and show that it indeed requires less queries to a reference policy. We observe a significant speed up in convergence, which we conjecture to be due to the effect of automated curriculum learning.

Key facts. Cited by 84 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 May 2016.

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Citing papers per year: 2018: 3; 2019: 3; 2020: 1; 2021: 16; 2022: 13; 2023: 14; 2024: 10; 2025: 16; 2026: 8.

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  1. 2023MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrationscited by 327
  2. 2018HG-DAgger: Interactive Imitation Learning with Human Expertscited by 266
  3. 2022VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priorscited by 136
  4. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 114
  5. 2021ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learningcited by 108
  6. 2018EnsembleDAgger: A Bayesian Approach to Safe Imitation Learningcited by 100
  7. 2020Autoregressive Knowledge Distillation through Imitation Learningcited by 74
  8. 2023Imitating Task and Motion Planning with Visuomotor Transformerscited by 66
  9. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 61
  10. 2024TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correctioncited by 59
  11. 2023Benchmarks and Algorithms for Offline Preference-Based Reward Learningcited by 56
  12. 2021LazyDAgger: Reducing Context Switching in Interactive Imitation Learningcited by 51
38 more
  1. 2022Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimizationcited by 49
  2. 2024Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Modelscited by 40
  3. 2021Safe Driving via Expert Guided Policy Optimizationcited by 31
  4. 2022Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervisioncited by 30
  5. 2023Human-in-the-Loop Task and Motion Planning for Imitation Learningcited by 29
  6. 2025RoboCopilot: Human-in-the-loop Interactive Imitation Learning for Robot Manipulationcited by 26
  7. 2022Eliciting Compatible Demonstrations for Multi-Human Imitation Learningcited by 23
  8. 2022Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learningcited by 22
  9. 2022When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learningcited by 21
  10. 2019Uncertainty-Aware Data Aggregation for Deep Imitation Learningcited by 20
  11. 2022PATO: Policy Assisted TeleOperation for Scalable Robot Data Collectioncited by 19
  12. 2023MEGA-DAgger: Imitation Learning with Multiple Imperfect Expertscited by 18
  13. 2021Offline Preference-Based Apprenticeship Learningcited by 17
  14. 2024SAFE-GIL: SAFEty Guided Imitation Learning for Robotic Systemscited by 14
  15. 2022Optimizing Crop Management with Reinforcement Learning and Imitation Learningcited by 13
  16. 2022A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racingcited by 12
  17. 2018OIL: Observational Imitation Learningcited by 11
  18. 2021Policy Gradient Bayesian Robust Optimization for Imitation Learningcited by 11
  19. 2021Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPCcited by 11
  20. 2024Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedbackcited by 11
  21. 2026OVD: On-policy Verbal Distillationcited by 11
  22. 2019Gaze Training by Modulated Dropout Improves Imitation Learningcited by 10
  23. 2019Simitate: A Hybrid Imitation Learning Benchmarkcited by 10
  24. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehiclescited by 10
  25. 2025Robot-Gated Interactive Imitation Learning with Adaptive Intervention Mechanismcited by 9
  26. 2021Multi-Task Learning with Sequence-Conditioned Transporter Networkscited by 8
  27. 2023How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvementcited by 8
  28. 2024A Comparison of Imitation Learning Algorithms for Bimanual Manipulationcited by 8
  29. 2025Genie Centurion: Accelerating Scalable Real-World Robot Training with Human Rewind-and-Refine Guidancecited by 7
  30. 2025RoaD: Rollouts as Demonstrations for Closed-Loop Supervised Fine-Tuning of Autonomous Driving Policiescited by 6
  31. 2023IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human Supervisorscited by 4
  32. 2024Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Drivingcited by 4
  33. 2026RoboPocket: Improve Robot Policies Instantly with Your Phonecited by 4
  34. 2026Scaling Self-Play for End-to-End Drivingcited by 4
  35. 2022Constrained Imitation Learning for a Flapping Wing Unmanned Aerial Vehiclecited by 3
  36. 2022A Joint Imitation-Reinforcement Learning Framework for Reduced Baseline Regretcited by 3
  37. 2021Affordance-based Reinforcement Learning for Urban Drivingcited by 2
  38. 2023Learning Complicated Manipulation Skills via Deterministic Policy with Limited Demonstrationscited by 2

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