mlsanityData to 14 Oct 2026

Experimental investigation of pose informed reinforcement learning for skid-steered visual navigation

arXiv 2506.21732 ↗First posted 26 Jun 2025cs.RO cs.AI cs.CVcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

Vision-based lane keeping is a topic of significant interest in the robotics and autonomous ground vehicles communities in various on-road and off-road applications. The skid-steered vehicle architecture has served as a useful vehicle platform for human controlled operations. However, systematic modeling, especially of the skid-slip wheel terrain interactions (primarily in off-road settings) has created bottlenecks for automation deployment. End-to-end learning based methods such as imitation learning and deep reinforcement learning, have gained prominence as a viable deployment option to counter the lack of accurate analytical models. However, the systematic formulation and subsequent verification/validation in dynamic operation regimes (particularly for skid-steered vehicles) remains a work in progress. To this end, a novel approach for structured formulation for learning visual navigation is proposed and investigated in this work. Extensive software simulations, hardware evaluations and ablation studies now highlight the significantly improved performance of the proposed approach against contemporary literature.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 14 of its 77 references that are in the map. First posted on arXiv 26 Jun 2025.

Builds on

14 of 77 references.

References · 14

  1. 2016End to End Learning for Self-Driving Carscited by 1,009
  2. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 413
  3. 2019End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordancescited by 149
  4. 2018CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-drivingcited by 97
  5. 2018End-to-End Race Driving with Deep Reinforcement Learningcited by 40
  6. 2024RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Drivingcited by 26
  7. 2023Regulated Pure Pursuit for Robot Path Trackingcited by 8
  8. 2020Evaluation of Skid-Steering Kinematic Models for Subarctic Environmentscited by 3
  9. 2023Vision-based DRL Autonomous Driving Agent with Sim2Real Transfercited by 2
  10. 2024Online identification of skidding modes with interactive multiple model estimationcited by 2
  11. 2021Dynamic Modeling and Simulation of a Four-wheel Skid-Steer Mobile Robot using Linear Graphscited by 1
  12. 2024A Probabilistic Motion Model for Skid-Steer Wheeled Mobile Robot Navigation on Off-Road Terrainscited by 1
2 more
  1. 2024Stabilization of vertical motion of a vehicle on bumpy terrain using deep reinforcement learningcited by 1
  2. 2025Characterizing gaussian mixture of motion modes for skid-steer vehicle state estimationcited by 1

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  1. 2019Off-road Autonomous Vehicles Traversability Analysis and Trajectory Planning Based on Deep Inverse Reinforcement Learning
  2. 2024An Examination of Offline-Trained Encoders in Vision-Based Deep Reinforcement Learning for Autonomous Driving
  3. 2026TADPO: Reinforcement Learning Goes Off-road
  4. 2026Adapting Reinforcement Learning for Path Planning in Constrained Parking Scenarios
  5. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
  6. 2024Reinforcement Learning for Wheeled Mobility on Vertically Challenging Terrain
  7. 2019Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective
  8. 2025BLIP-FusePPO: A Vision-Language Deep Reinforcement Learning Framework for Lane Keeping in Autonomous Vehicles