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Learning Sampling Distributions for Robot Motion Planning

arXiv 1709.05448 ↗First posted 16 Sep 2017cs.RO cs.LGcited by 79

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Abstract (arXiv)

A defining feature of sampling-based motion planning is the reliance on an implicit representation of the state space, which is enabled by a set of probing samples. Traditionally, these samples are drawn either probabilistically or deterministically to uniformly cover the state space. Yet, the motion of many robotic systems is often restricted to "small" regions of the state space, due to, for example, differential constraints or collision-avoidance constraints. To accelerate the planning process, it is thus desirable to devise non-uniform sampling strategies that favor sampling in those regions where an optimal solution might lie. This paper proposes a methodology for non-uniform sampling, whereby a sampling distribution is learned from demonstrations, and then used to bias sampling. The sampling distribution is computed through a conditional variational autoencoder, allowing sample generation from the latent space conditioned on the specific planning problem. This methodology is general, can be used in combination with any sampling-based planner, and can effectively exploit the underlying structure of a planning problem while maintaining the theoretical guarantees of sampling-based approaches. Specifically, on several planning problems, the proposed methodology is shown to effectively learn representations for the relevant regions of the state space, resulting in an order of magnitude improvement in terms of success rate and convergence to the optimal cost.

Key facts. Cited by 79 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Sep 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 9; 2022: 19; 2023: 11; 2024: 18; 2025: 11; 2026: 10.

79 papers.

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  1. 2021Habitat 2.0: Training Home Assistants to Rearrange their Habitatcited by 501
  2. 2023Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Modelscited by 155
  3. 2022Motion Policy Networkscited by 53
  4. 2025Simultaneous Multi-Robot Motion Planning with Projected Diffusion Modelscited by 37
  5. 2024Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrationscited by 35
  6. 2022Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Spacecited by 31
  7. 2021Differentiable Spatial Planning using Transformerscited by 25
  8. 2022NTFields: Neural Time Fields for Physics-Informed Robot Motion Planningcited by 22
  9. 2022Learning Implicit Priors for Motion Optimizationcited by 20
  10. 2021Parallelised Diffeomorphic Sampling-based Motion Planningcited by 16
  11. 2022Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networkscited by 16
  12. 2024Transformer-based Model Predictive Control: Trajectory Optimization via Sequence Modelingcited by 15
38 more
  1. 2024FRENETIX: A High-Performance and Modular Motion Planning Framework for Autonomous Drivingcited by 14
  2. 2022Variational Inference MPC using Normalizing Flows and Out-of-Distribution Projectioncited by 13
  3. 2024PRESTO: Fast Motion Planning Using Diffusion Models Based on Key-Configuration Environment Representationcited by 11
  4. 2021Motion Planning Transformers: A Motion Planning Framework for Mobile Robotscited by 10
  5. 2022Accelerating Integrated Task and Motion Planning with Neural Feasibility Checkingcited by 10
  6. 2023Progressive Learning for Physics-informed Neural Motion Planningcited by 8
  7. 2021MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning under Kinodynamic Constraintscited by 7
  8. 2023Graph Reinforcement Learning for Network Control via Bi-Level Optimizationcited by 7
  9. 2022CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spacescited by 6
  10. 2022Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigationcited by 6
  11. 2022A Survey on the Integration of Machine Learning with Sampling-based Motion Planningcited by 6
  12. 2022Dynamic Conditional Imitation Learning for Autonomous Drivingcited by 6
  13. 2023Neural Field Dynamics Model for Granular Object Piles Manipulationcited by 6
  14. 2025Physics-informed Temporal Difference Metric Learning for Robot Motion Planningcited by 6
  15. 2022Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encodingcited by 5
  16. 2022Control Transformer: Robot Navigation in Unknown Environments through PRM-Guided Return-Conditioned Sequence Modelingcited by 5
  17. 2023Predicting Motion Plans for Articulating Everyday Objectscited by 5
  18. 2023Contrastive Initial State Buffer for Reinforcement Learningcited by 5
  19. 2024Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controllercited by 5
  20. 2020Learning from demonstration using products of experts: applications to manipulation and task prioritizationcited by 4
  21. 2021DiffCo: Auto-Differentiable Proxy Collision Detection with Multi-class Labels for Safety-Aware Trajectory Optimizationcited by 4
  22. 2023Leveraging Scene Embeddings for Gradient-Based Motion Planning in Latent Spacecited by 4
  23. 2023Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learningcited by 4
  24. 2024Offline Hierarchical Reinforcement Learning via Inverse Optimizationcited by 4
  25. 2025Cascaded Diffusion Models for Neural Motion Planningcited by 4
  26. 2025Warm-Starting Optimization-Based Motion Planning for Robotic Manipulators via Point Cloud-Conditioned Flow Matchingcited by 4
  27. 2022MLNav: Learning to Safely Navigate on Martian Terrainscited by 3
  28. 2024Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environmentscited by 3
  29. 2025Train-Once Plan-Anywhere Kinodynamic Motion Planning via Diffusion Treescited by 3
  30. 2022Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planningcited by 2
  31. 2022L4KDE: Learning for KinoDynamic Tree Expansioncited by 2
  32. 2022Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path constructioncited by 2
  33. 2022Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planningcited by 2
  34. 2022Robot Motion Planning as Video Prediction: A Spatio-Temporal Neural Network-based Motion Plannercited by 2
  35. 2023Improving Path Planning Performance through Multimodal Generative Models with Local Criticscited by 2
  36. 2024Global Tensor Motion Planningcited by 2
  37. 2025Generative Models From and For Sampling-Based MPC: A Bootstrapped Approach For Adaptive Contact-Rich Manipulationcited by 2
  38. 2021CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based RLcited by 1

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