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A Survey on the Integration of Machine Learning with Sampling-based Motion Planning

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Real‐time density‐based crowd simulation

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A framework for maximum likelihood parameter identification applied on MAVs

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A Machine Learning Approach for Feature-Sensitive Motion Planning

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Collision Detection or Nearest-Neighbor Search? On the Computational Bottleneck in Sampling-based Motion Planning

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Fast Swept Volume Estimation with Deep Learning

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Belief-Space Planning Using Learned Models with Application to Underactuated Hands

inferredcites →https://doi.org/10.1007/978-3-319-29363-9_17openalex-snapshot

Demonstration-Guided Motion Planning

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Reproducing Expert-Like Motion in Deformable Environments Using Active Learning and IOC

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Reinforcement Learning in Robotics: A Survey

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Faster Sample-Based Motion Planning Using Instance-Based Learning

inferredcites →https://doi.org/10.1007/978-3-642-41610-1_172-1openalex-snapshot

Asymptotically Optimal Sampling-Based Planners

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Nonholonomic multibody mobile robots: Controllability and motion planning in the presence of obstacles

inferredcites →https://doi.org/10.1007/s10514-013-9334-3openalex-snapshot

Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns

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Automated aerial suspended cargo delivery through reinforcement learning

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Probabilistic roadmaps for path planning in high-dimensional configuration spaces

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Hidden Markov model for dynamic obstacle avoidance of mobile robot navigation

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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

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Learning approximate cost-to-go metrics to improve sampling-based motion planning

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A robot path planning framework that learns from experience

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Implementation of an embodied general reinforcement learner on a serial link manipulator

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Use of relaxation methods in sampling-based algorithms for optimal motion planning

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Design and evolution of a modular tensegrity robot platform

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Distance metric learning for RRT-based motion planning with constant-time inference

inferredcites →https://doi.org/10.1109/icra.2015.7139284openalex-snapshot

Experience-based planning with sparse roadmap spanners

inferredcites →https://doi.org/10.1109/icra.2015.7139517openalex-snapshot

The planner ensemble: Motion planning by executing diverse algorithms

inferredcites →https://doi.org/10.1109/icra.2015.7139603openalex-snapshot

Lazy collision checking in asymptotically-optimal motion planning

inferredcites →https://doi.org/10.1109/icra.2016.7487116openalex-snapshot

Learning high-dimensional Mixture Models for fast collision detection in Rapidly-Exploring Random Trees

inferredcites →https://doi.org/10.1109/icra.2017.7989453openalex-snapshot

Sampling-based motion planning for active multirotor system identification

inferredcites →https://doi.org/10.1109/icra.2018.8460730openalex-snapshot

Learning Sampling Distributions for Robot Motion Planning

inferredcites →https://doi.org/10.1109/icra.2018.8461096openalex-snapshot

PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-Based Planning

inferredcites →https://doi.org/10.1109/icra.2019.8793889openalex-snapshot

Motion Planning Networks

inferredcites →https://doi.org/10.1109/icra.2019.8794317openalex-snapshot

Using Local Experiences for Global Motion Planning

inferredcites →https://doi.org/10.1109/icra40945.2020.9196564openalex-snapshot

Motion Planning with Competency-Aware Transition Models for Underactuated Adaptive Hands

inferredcites →https://doi.org/10.1109/icra40945.2020.9196771openalex-snapshot

Learned Sampling Distributions for Efficient Planning in Hybrid Geometric and Object-Level Representations

inferredcites →https://doi.org/10.1109/icra40945.2020.9197014openalex-snapshot

Posterior Sampling for Anytime Motion Planning on Graphs with Expensive-to-Evaluate Edges

inferredcites →https://doi.org/10.1109/icra40945.2020.9197106openalex-snapshot

Learned Critical Probabilistic Roadmaps for Robotic Motion Planning

inferredcites →https://doi.org/10.1109/icra40945.2020.9197236openalex-snapshot

Refined Analysis of Asymptotically-Optimal Kinodynamic Planning in the State-Cost Space

inferredcites →https://doi.org/10.1109/icra46639.2022.9812057openalex-snapshot

Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordances

inferredcites →https://doi.org/10.1109/icra46639.2022.9812076openalex-snapshot

Learning to Retrieve Relevant Experiences for Motion Planning

inferredcites →https://doi.org/10.1109/icra48506.2021.9561104openalex-snapshot

Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions

inferredcites →https://doi.org/10.1109/icra48506.2021.9561672openalex-snapshot

Cost-to-Go Function Generating Networks for High Dimensional Motion Planning

inferredcites →https://doi.org/10.1109/icra48506.2021.9561684openalex-snapshot

A Knowledge-Based Fast Motion Planning Method Through Online Environmental Feature Learning

inferredcites →https://doi.org/10.1109/iros.2008.4650959openalex-snapshot

Probabilistic navigation in dynamic environment using Rapidly-exploring Random Trees and Gaussian processes

inferredcites →https://doi.org/10.1109/iros.2010.5651569openalex-snapshot

Non-parametric learning for natural plan generation

inferredcites →https://doi.org/10.1109/iros.2013.6696510openalex-snapshot

Adaptive neighbor connection for PRMs: A natural fit for heterogeneous environments and parallelism

inferredcites →https://doi.org/10.1109/iros.2013.6696513openalex-snapshot

Free-configuration biased sampling for motion planning

inferredcites →https://doi.org/10.1109/iros.2014.6942569openalex-snapshot

Distance metric approximation for state-space RRTs using supervised learning

inferredcites →https://doi.org/10.1109/iros.2015.7353738openalex-snapshot

Machine learning guided exploration for sampling-based motion planning algorithms

inferredcites →https://doi.org/10.1109/iros.2018.8593672openalex-snapshot

Efficient and Asymptotically Optimal Kinodynamic Motion Planning via Dominance-Informed Regions

inferredcites →https://doi.org/10.1109/iros40897.2019.8968503openalex-snapshot

LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning

inferredcites →https://doi.org/10.1109/iros45743.2020.9341283openalex-snapshot

Dynamically Constrained Motion Planning Networks for Non-Holonomic Robots

inferredcites →https://doi.org/10.1109/iros45743.2020.9341312openalex-snapshot

PLRC*: A piecewise linear regression complex for approximating optimal robot motion

inferredcites →https://doi.org/10.1109/iros51168.2021.9636236openalex-snapshot

Using Experience to Improve Constrained Planning on Foliations for Multi-Modal Problems

inferredcites →https://doi.org/10.1109/iros51168.2021.9636651openalex-snapshot

HyperPlan: A Framework for Motion Planning Algorithm Selection and Parameter Optimization

inferredcites →https://doi.org/10.1109/iros51168.2021.9636657openalex-snapshot

Improving Kinodynamic Planners for Vehicular Navigation with Learned Goal-Reaching Controllers

inferredcites →https://doi.org/10.1109/iros51168.2021.9636864openalex-snapshot

Learning When to Quit: Meta-Reasoning for Motion Planning

inferredcites →https://doi.org/10.1109/jas.2014.7004690openalex-snapshot

Continuous action reinforcement learning for control-affine systems with unknown dynamics

inferredcites →https://doi.org/10.1109/lra.2018.2801462openalex-snapshot

Distance-Aware Dynamically Weighted Roadmaps for Motion Planning in Unknown Environments

inferredcites →https://doi.org/10.1109/lra.2019.2894861openalex-snapshot

Learning Feasibility for Task and Motion Planning in Tabletop Environments

inferredcites →https://doi.org/10.1109/lra.2019.2931199openalex-snapshot

RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators From RL Policies

inferredcites →https://doi.org/10.1109/lra.2021.3067847openalex-snapshot

MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning Under Kinodynamic Constraints

inferredcites →https://doi.org/10.1109/lra.2021.3133603openalex-snapshot

MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets

inferredcites →https://doi.org/10.1109/robot.2000.844107openalex-snapshot

Path planning using lazy PRM

inferredcites →https://doi.org/10.1109/robot.2000.844730openalex-snapshot

RRT-connect: An efficient approach to single-query path planning

inferredcites →https://doi.org/10.1109/robot.2005.1570432openalex-snapshot

Motion Planning for Mobile Manipulators along Given End-effector Paths

inferredcites →https://doi.org/10.1109/robot.2005.1570590openalex-snapshot

Sampling-Based Motion Planning Using Predictive Models

inferredcites →https://doi.org/10.1109/robot.2005.1570712openalex-snapshot

Hybrid PRM Sampling with a Cost-Sensitive Adaptive Strategy

inferredcites →https://doi.org/10.1109/robot.2005.1570825openalex-snapshot

Learning-Assisted Multi-Step Planning

inferredcites →https://doi.org/10.1109/robot.2006.1641878openalex-snapshot

RRT-blossom: RRT with a local flood-fill behavior

inferredcites →https://doi.org/10.1109/robot.2007.363873openalex-snapshot

Faster Motion Planning Using Learned Local Viability Models

inferredcites →https://doi.org/10.1109/robot.2008.4543787openalex-snapshot

Adaptive workspace biasing for sampling-based planners

inferredcites →https://doi.org/10.1109/robot.2009.5152817openalex-snapshot

CHOMP: Gradient optimization techniques for efficient motion planning

inferredcites →https://doi.org/10.1109/sfcs.1979.10openalex-snapshot

Complexity of the mover's problem and generalizations

inferredcites →https://doi.org/10.1109/tase.2020.2976560openalex-snapshot

Neural RRT*: Learning-Based Optimal Path Planning

inferredcites →https://doi.org/10.1109/tie.2018.2816000openalex-snapshot

Neural Network Approximation Based Near-Optimal Motion Planning With Kinodynamic Constraints Using RRT

inferredcites →https://doi.org/10.1109/tro.2016.2602363openalex-snapshot

Asymptotically Optimal Planning by Feasible Kinodynamic Planning in a State–Cost Space

inferredcites →https://doi.org/10.1109/tro.2020.2974094openalex-snapshot

Learning-Based Proxy Collision Detection for Robot Motion Planning Applications

inferredcites →https://doi.org/10.1109/tro.2021.3096070openalex-snapshot

Constrained Motion Planning Networks X

inferredcites →https://doi.org/10.1145/1315184.1315201openalex-snapshot

Real-time navigation of independent agents using adaptive roadmaps

inferredcites →https://doi.org/10.1145/1401132.1401209openalex-snapshot

Planning collision-free reaching motions for interactive object manipulation and grasping

inferredcites →https://doi.org/10.1145/369133.369239openalex-snapshot

Using motion planning to study protein folding pathways

inferredcites →https://doi.org/10.1177/0278364911406761openalex-snapshot

Sampling-based algorithms for optimal motion planning

inferredcites →https://doi.org/10.1177/0278364915577958openalex-snapshot

Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions

inferredcites →https://doi.org/10.1177/0278364915614386openalex-snapshot

Asymptotically optimal sampling-based kinodynamic planning

inferredcites →https://doi.org/10.1177/0278364915625345openalex-snapshot

Efficient collision checking in sampling-based motion planning via safety certificates

inferredcites →https://doi.org/10.1177/0278364918770733openalex-snapshot

The limits and potentials of deep learning for robotics

inferredcites →https://doi.org/10.1177/0278364918790369openalex-snapshot

Continuous-time Gaussian process motion planning via probabilistic inference

inferredcites →https://doi.org/10.1177/0278364919848837openalex-snapshot

Learning to guide task and motion planning using score-space representation

inferredcites →https://doi.org/10.1177/0278364919868530openalex-snapshot

Exploring implicit spaces for constrained sampling-based planning

inferredcites →https://doi.org/10.1177/02783649211004615openalex-snapshot

Learning compositional models of robot skills for task and motion planning

inferredcites →https://doi.org/10.1186/s12918-016-0297-9openalex-snapshot

Adaptive local learning in sampling based motion planning for protein folding

inferredcites →https://doi.org/10.15607/rss.2019.xv.026openalex-snapshot

Harnessing Reinforcement Learning for Neural Motion Planning

inferredcites →https://doi.org/10.15607/rss.2019.xv.050openalex-snapshot

Leveraging Experience in Lazy Search

inferredcites →https://doi.org/10.15607/rss.2020.xvi.001openalex-snapshot

Planning and Execution using Inaccurate Models with Provable Guarantees

inferredcites →https://doi.org/10.15607/rss.2021.xvii.064openalex-snapshot

Learning Proofs of Motion Planning Infeasibility

inferredcites →https://doi.org/10.1609/aaai.v34i01.5473openalex-snapshot

A Novel Learning Framework for Sampling-Based Motion Planning in Autonomous Driving

inferredcites →https://doi.org/10.24963/ijcai.2018/679openalex-snapshot

Bayesian Active Edge Evaluation on Expensive Graphs

inferredcites →https://doi.org/10.2514/6.2021-1754openalex-snapshot

Sampling-based Kinodynamic Motion Planning Using a Neural Network Controller

inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot

Exploiting Generative AI to Scale up Intelligent Tutoring Systems

inferredcites →https://doi.org/10.48550/arxiv.1706.09351openalex-snapshot

Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs

inferredcites →https://doi.org/10.48550/arxiv.1807.10366openalex-snapshot

Robot Motion Planning in Learned Latent Spaces

inferredcites →https://doi.org/10.48550/arxiv.1903.00070openalex-snapshot

Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees

inferredcites →https://doi.org/10.48550/arxiv.1907.03146openalex-snapshot

A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms

inferredcites →https://doi.org/10.48550/arxiv.2008.11174openalex-snapshot

Learning Obstacle Representations for Neural Motion Planning

inferredcites →https://doi.org/10.48550/arxiv.2009.11465openalex-snapshot

Model Identification and Control of a Low-Cost Wheeled Mobile Robot Using Differentiable Physics

inferredcites →https://doi.org/10.48550/arxiv.2010.06491openalex-snapshot

Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning

inferredcites →https://doi.org/10.48550/arxiv.2210.08864openalex-snapshot

Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s42979-024-02640-8openalex-snapshot

Reliable and Accurate Implicit Neural Representation of Multiple Swept Volumes with Application to Safe Human–Robot Interaction

inferred← citeshttps://doi.org/10.1007/s44443-026-01320-2openalex-snapshot

An improved APF-guided BiRRT* motion planner for autonomous robotic apple harvesting in cluttered orchard environments

inferred← citeshttps://doi.org/10.1016/j.robot.2026.105334openalex-snapshot

Enhancing sampling-based planning with a library of paths

inferred← citeshttps://doi.org/10.1017/s0263574726103932openalex-snapshot

Deep learning-based framework for efficient multigoal shortest path planning in indoor environments

inferred← citeshttps://doi.org/10.1109/access.2025.3552225openalex-snapshot

Data-Driven Motion Planning: A Survey on Deep Neural Networks, Reinforcement Learning, and Large Language Model Approaches

inferred← citeshttps://doi.org/10.1109/aris59192.2023.10268533openalex-snapshot

Fast Collision Detection for Robot Manipulator Path: an Approach Based on Implicit Neural Representation of Multiple Swept Volumes

inferred← citeshttps://doi.org/10.1109/ccdc62350.2024.10587648openalex-snapshot

Hybrid path planning and following of a quadrotor UAV based on deep reinforcement learning

inferred← citeshttps://doi.org/10.1109/icra55743.2025.11128311openalex-snapshot

From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection

inferred← citeshttps://doi.org/10.1109/icra55743.2025.11128438openalex-snapshot

Multi-Goal Motion Memory

inferred← citeshttps://doi.org/10.1109/icra57147.2024.10610790openalex-snapshot

Motion Memory: Leveraging Past Experiences to Accelerate Future Motion Planning

inferred← citeshttps://doi.org/10.1109/iros58592.2024.10802619openalex-snapshot

Roadmaps with Gaps over Controllers: Achieving Efficiency in Planning under Dynamics

inferred← citeshttps://doi.org/10.1109/lra.2026.3699250openalex-snapshot

Social Robot Navigation Under Kinodynamic Constraints Using Learning-Informed Sampling for Indoor Environments

inferred← citeshttps://doi.org/10.1109/tase.2026.3660830openalex-snapshot

Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities

inferred← citeshttps://doi.org/10.1109/tits.2025.3558958openalex-snapshot

How to Enhance the Interpretability of Learning-Based Motion Planning for Intelligent Vehicles—A Survey

inferred← citeshttps://doi.org/10.1145/3712255.3734320openalex-snapshot

On the Parallels Between Evolutionary Theory and the State of AI

inferred← citeshttps://doi.org/10.1146/annurev-control-061623-094742openalex-snapshot

Sampling-Based Motion Planning: A Comparative Review

inferred← citeshttps://doi.org/10.15607/rss.2023.xix.060openalex-snapshot

Motion Planning (In)feasibility Detection using a Prior Roadmap via Path and Cut Search

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Learning-Based Sampling Strategies Leveraging Bidirectional Motion Planning Characteristics

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