mlsanityData to 14 Oct 2026

Deep Imitative Models for Flexible Inference, Planning, and Control

arXiv 1810.06544 ↗First posted 15 Oct 2018cs.LG cs.AI cs.CVcited by 76

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

Abstract (arXiv)

Imitation Learning (IL) is an appealing approach to learn desirable autonomous behavior. However, directing IL to achieve arbitrary goals is difficult. In contrast, planning-based algorithms use dynamics models and reward functions to achieve goals. Yet, reward functions that evoke desirable behavior are often difficult to specify. In this paper, we propose Imitative Models to combine the benefits of IL and goal-directed planning. Imitative Models are probabilistic predictive models of desirable behavior able to plan interpretable expert-like trajectories to achieve specified goals. We derive families of flexible goal objectives, including constrained goal regions, unconstrained goal sets, and energy-based goals. We show that our method can use these objectives to successfully direct behavior. Our method substantially outperforms six IL approaches and a planning-based approach in a dynamic simulated autonomous driving task, and is efficiently learned from expert demonstrations without online data collection. We also show our approach is robust to poorly specified goals, such as goals on the wrong side of the road.

Key facts. Cited by 76 papers in the map (data through 14 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. First posted on arXiv 15 Oct 2018.

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Citing papers per year: 2019: 3; 2020: 4; 2021: 24; 2022: 14; 2023: 15; 2024: 7; 2025: 4; 2026: 5.

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  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  2. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,061
  3. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Drivingcited by 485
  4. 2021Multi-Modal Fusion Transformer for End-to-End Autonomous Drivingcited by 377
  5. 2022Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baselinecited by 248
  6. 2023Parting with Misconceptions about Learning-based Vehicle Motion Planningcited by 242
  7. 2021MP3: A Unified Model to Map, Perceive, Predict and Plancited by 212
  8. 2021End-to-End Urban Driving by Imitating a Reinforcement Learning Coachcited by 194
  9. 2022Model-Based Imitation Learning for Urban Drivingcited by 150
  10. 2023GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Drivingcited by 141
  11. 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarioscited by 129
  12. 2021Rapid Exploration for Open-World Navigation with Latent Goal Modelscited by 121
38 more
  1. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 103
  2. 2021Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Taskscited by 101
  3. 2019Urban Driving with Conditional Imitation Learningcited by 86
  4. 2022Differentiable Raycasting for Self-supervised Occupancy Forecastingcited by 84
  5. 2021VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehiclescited by 81
  6. 2020Deep Imitation Learning for Bimanual Robotic Manipulationcited by 50
  7. 2022Hierarchical Model-Based Imitation Learning for Planning in Autonomous Drivingcited by 44
  8. 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
  9. 2023DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learningcited by 28
  10. 2021Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Drivingcited by 27
  11. 2021PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learningcited by 23
  12. 2021Causal Navigation by Continuous-time Neural Networkscited by 23
  13. 2021Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Modelscited by 22
  14. 2021Learning by Watchingcited by 22
  15. 2024AlignIQL: Policy Alignment in Implicit Q-Learning through Constrained Optimizationcited by 19
  16. 2024Hybrid-Prediction Integrated Planning for Autonomous Drivingcited by 18
  17. 2021Learning Interactive Driving Policies via Data-driven Simulationcited by 17
  18. 2024DeTra: A Unified Model for Object Detection and Trajectory Forecastingcited by 15
  19. 2019Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigationcited by 13
  20. 2021Imitation by Predicting Observationscited by 13
  21. 2023A Survey of Demonstration Learningcited by 12
  22. 2024QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Drivingcited by 11
  23. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehiclescited by 10
  24. 2022Generative Planning for Temporally Coordinated Exploration in Reinforcement Learningcited by 10
  25. 2023Residual Q-Learning: Offline and Online Policy Customization without Valuecited by 10
  26. 2021Self-Supervised Action-Space Prediction for Automated Drivingcited by 9
  27. 2020Driving Through Ghosts: Behavioral Cloning with False Positivescited by 8
  28. 2023Occupancy Prediction-Guided Neural Planner for Autonomous Drivingcited by 8
  29. 2019Integration of Imitation Learning using GAIL and Reinforcement Learning using Task-achievement Rewards via Probabilistic Graphical Modelcited by 7
  30. 2021Causal Imitative Model for Autonomous Drivingcited by 7
  31. 2024Residual-MPPI: Online Policy Customization for Continuous Controlcited by 7
  32. 2020SAFARI: Safe and Active Robot Imitation Learning with Imaginationcited by 5
  33. 2021End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centeringcited by 5
  34. 2021Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Mapcited by 4
  35. 2023Get Back Here: Robust Imitation by Return-to-Distribution Planningcited by 4
  36. 2021DROMO: Distributionally Robust Offline Model-based Policy Optimizationcited by 3
  37. 2022Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planningcited by 3
  38. 2022Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Drivingcited by 3

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  1. 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
  2. 2024Imitating Cost-Constrained Behaviors in Reinforcement Learning
  3. 2025Exposing the Copycat Problem of Imitation-based Planner: A Novel Closed-Loop Simulator, Causal Benchmark and Joint IL-RL Baseline
  4. 2025Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advances
  5. 2025Sequence of Expert: Boosting Imitation Planners for Autonomous Driving through Temporal Alternation
  6. 2022Divide & Conquer Imitation Learning
  7. 2025Consistent Zero-Shot Imitation with Contrastive Goal Inference
  8. 2025From Imitation to Optimization: A Comparative Study of Offline Learning for Autonomous Driving