mlsanityData to 7 Oct 2026

From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots

arXiv 1609.07910 ↗First posted 26 Sep 2016cs.ROcited by 37

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Abstract (arXiv)

Learning from demonstration for motion planning is an ongoing research topic. In this paper we present a model that is able to learn the complex mapping from raw 2D-laser range findings and a target position to the required steering commands for the robot. To our best knowledge, this work presents the first approach that learns a target-oriented end-to-end navigation model for a robotic platform. The supervised model training is based on expert demonstrations generated in simulation with an existing motion planner. We demonstrate that the learned navigation model is directly transferable to previously unseen virtual and, more interestingly, real-world environments. It can safely navigate the robot through obstacle-cluttered environments to reach the provided targets. We present an extensive qualitative and quantitative evaluation of the neural network-based motion planner, and compare it to a grid-based global approach, both in simulation and in real-world experiments.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Sep 2016.

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Citing papers per year: 2020: 1; 2021: 11; 2022: 6; 2023: 3; 2024: 7; 2025: 7; 2026: 2.

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  1. 2022ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learningcited by 365
  2. 2021Deep Federated Learning for Autonomous Drivingcited by 54
  3. 2021Learning a State Representation and Navigation in Cluttered and Dynamic Environmentscited by 30
  4. 2022Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigationcited by 27
  5. 2022VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamicscited by 19
  6. 2020APPLD: Adaptive Planner Parameter Learning from Demonstrationcited by 17
  7. 2021Learning Interactive Driving Policies via Data-driven Simulationcited by 17
  8. 2021Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learningcited by 11
  9. 2021Curriculum Learning for Safe Mapless Navigationcited by 11
  10. 2022High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimizationcited by 9
  11. 2021Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulationcited by 8
  12. 2021Human-Inspired Multi-Agent Navigation using Knowledge Distillationcited by 7
25 more
  1. 2022Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigationcited by 6
  2. 2024Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controllercited by 5
  3. 2024Grounded Curriculum Learningcited by 5
  4. 2023Leveraging Scene Embeddings for Gradient-Based Motion Planning in Latent Spacecited by 4
  5. 2023A Study on Learning Social Robot Navigation with Multimodal Perceptioncited by 4
  6. 2024QuadrupedGPT: Towards a Versatile Quadruped Agent in Open-ended Worldscited by 4
  7. 2022MLNav: Learning to Safely Navigate on Martian Terrainscited by 3
  8. 2025MaskPlanner: Learning-Based Object-Centric Motion Generation from 3D Point Cloudscited by 3
  9. 2025Data-Efficient Learning from Human Interventions for Mobile Robotscited by 3
  10. 2024CURE: Simulation-Augmented Auto-Tuning in Roboticscited by 2
  11. 2025Socially aware navigation for mobile robots: a survey on deep reinforcement learning approachescited by 2
  12. 2021Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental Conditionscited by 1
  13. 2021Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approachcited by 1
  14. 2024Transformer-Enhanced Motion Planner: Attention-Guided Sampling for State-Specific Decision Makingcited by 1
  15. 2025Imitation Learning for Obstacle Avoidance Using End-to-End CNN-Based Sensor Fusioncited by 1
  16. 2025Accelerated Multi-Modal Motion Planning Using Context-Conditioned Diffusion Modelscited by 1
  17. 2026Learning from Demonstration with Failure Awareness for Safe Robot Navigationcited by 1
  18. 2021The Pitfall of More Powerful Autoencoders in Lidar-Based Navigationcited by 0
  19. 2021Autonomous Navigation in Dynamic Environments: Deep Learning-Based Approachcited by 0
  20. 2023Improving Reliable Navigation under Uncertainty via Predictions Informed by Non-Local Informationcited by 0
  21. 2024Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucinationcited by 0
  22. 2024RoboArm-NMP: a Learning Environment for Neural Motion Planningcited by 0
  23. 2025Are Learning-Based Approaches Ready for Real-World Indoor Navigation? A Case for Imitation Learningcited by 0
  24. 2025DRL-TH: Jointly Utilizing Temporal Graph Attention and Hierarchical Fusion for UGV Navigation in Crowded Environmentscited by 0
  25. 2026SPADE: Sketch-guided Path Planning Augmented with Diffusion Expertscited by 0