mlsanityData to 14 Oct 2026

Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning

arXiv 2004.08051 ↗First posted 17 Apr 2020cs.RO cs.AI cs.CVcited by 6

On the map: Offline RL and imitation learning from data → Imitation and offline RL for driving

Abstract (arXiv)

In this work, we present a method for obtaining an implicit objective function for vision-based navigation. The proposed methodology relies on Imitation Learning, Model Predictive Control (MPC), and an interpretation technique used in Deep Neural Networks. We use Imitation Learning as a means to do Inverse Reinforcement Learning in order to create an approximate cost function generator for a visual navigation challenge. The resulting cost function, the costmap, is used in conjunction with MPC for real-time control and outperforms other state-of-the-art costmap generators in novel environments. The proposed process allows for simple training and robustness to out-of-sample data. We apply our method to the task of vision-based autonomous driving in multiple real and simulated environments and show its generalizability.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data. Builds on 15 of its 37 references that are in the map. First posted on arXiv 17 Apr 2020.

Builds on

15 of 37 references.

References · 15

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
  3. 2016End to End Learning for Self-Driving Carscited by 1,009
  4. 2015End-to-End Training of Deep Visuomotor Policiescited by 875
  5. 2015Evaluating the visualization of what a Deep Neural Network has learnedcited by 419
  6. 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 345
  7. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 298
  8. 2015DeepDriving: Learning Affordance for Direct Perception in Autonomous Drivingcited by 231
  9. 2016End-to-end Learning of Driving Models from Large-scale Video Datasetscited by 148
  10. 2019Deep Drone Racing: From Simulation to Reality with Domain Randomizationcited by 76
  11. 2019ADAPS: Autonomous Driving Via Principled Simulationscited by 15
  12. 2018Ensemble Bayesian Decision Making with Redundant Deep Perceptual Control Policiescited by 13
3 more
  1. 2019Perceptual Attention-based Predictive Controlcited by 11
  2. 2018Vision-Based High Speed Driving with a Deep Dynamic Observercited by 7
  3. 2020Aggressive Perception-Aware Navigation using Deep Optical Flow Dynamics and PixelMPCcited by 6

Built on by

Cited by year

Citing papers per year: 2020: 1; 2021: 0; 2022: 2; 2023: 1; 2024: 0; 2025: 2.

6 papers.

  1. 2020Model-Based Inverse Reinforcement Learning from Visual Demonstrationscited by 52
  2. 2022Spatiotemporal Costmap Inference for MPC via Deep Inverse Reinforcement Learningcited by 10
  3. 2025RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for Vision-Based Drone Navigationcited by 6
  4. 2022Output Feedback Tube MPC-Guided Data Augmentation for Robust, Efficient Sensorimotor Policy Learningcited by 2
  5. 2025Model Predictive Adversarial Imitation Learning for Planning from Observationcited by 2
  6. 2023Tube-NeRF: Efficient Imitation Learning of Visuomotor Policies from MPC using Tube-Guided Data Augmentation and NeRFscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2018Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrations
  2. 2022Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation
  3. 2020Learning Navigation Costs from Demonstration in Partially Observable Environments
  4. 2020Learning Navigation Costs from Demonstration with Semantic Observations
  5. 2023Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation
  6. 2019Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving
  7. 2019Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective
  8. 2021Inverse reinforcement learning for autonomous navigation via differentiable semantic mapping and planning