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Harnessing Reinforcement Learning for Neural Motion Planning
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-29363-9_18openalex-snapshot
Learning from Experience in Manipulation Planning: Setting the Right Goals
inferredcites →https://doi.org/10.1016/0893-6080(94)e0045-mopenalex-snapshot
Neural Network Dynamics for Path Planning and Obstacle Avoidance
inferredcites →https://doi.org/10.1016/j.neunet.2005.01.004openalex-snapshot
The dynamic wave expansion neural network model for robot motion planning in time-varying environments
inferredcites →https://doi.org/10.1016/s0893-6080(99)00103-3openalex-snapshot
An efficient neural network approach to dynamic robot motion planning
inferredcites →https://doi.org/10.1038/nature14236openalex-snapshot
Human-level control through deep reinforcement learning
inferredcites →https://doi.org/10.1038/nature16961openalex-snapshot
Mastering the game of Go with deep neural networks and tree search
inferredcites →https://doi.org/10.1098/rstb.2002.1258openalex-snapshot
Computational approaches to motor learning by imitation
inferredcites →https://doi.org/10.1109/70.508439openalex-snapshot
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
inferredcites →https://doi.org/10.1109/coase.2007.4341761openalex-snapshot
Offline and Online Evolutionary Bi-Directional RRT Algorithms for Efficient Re-Planning in Dynamic Environments
inferredcites →https://doi.org/10.1109/icra.2012.6224742openalex-snapshot
A robot path planning framework that learns from experience
inferredcites →https://doi.org/10.1109/icra.2017.7989182openalex-snapshot
From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots
inferredcites →https://doi.org/10.1109/iros.2018.8593772openalex-snapshot
Deeply Informed Neural Sampling for Robot Motion Planning
inferredcites →https://doi.org/10.1109/iros.2018.8593947openalex-snapshot
Deep Sequential Models for Sampling-Based Planning
inferredcites →https://doi.org/10.1109/lra.2018.2869644openalex-snapshot
Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Mapless Navigation by Leveraging Prior Demonstrations
inferredcites →https://doi.org/10.1109/mra.2012.2205651openalex-snapshot
The Open Motion Planning Library
inferredcites →https://doi.org/10.1109/robot.2008.4543392openalex-snapshot
Path and trajectory diversity: Theory and algorithms
inferredcites →https://doi.org/10.1109/robot.2010.5509825openalex-snapshot
Trajectory prediction in cluttered voxel environments
inferredcites →https://doi.org/10.1109/tnn.2004.842673openalex-snapshot
Reinforcement Learning: An Introduction
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1177/0278364911406761openalex-snapshot
Sampling-based algorithms for optimal motion planning
inferredcites →https://doi.org/10.1184/r1/6550949openalex-snapshot
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
inferredcites →https://doi.org/10.1184/r1/6603146openalex-snapshot
ALVINN, an autonomous land vehicle in a neural network
inferredcites →https://doi.org/10.1609/icaps.v28i1.13872openalex-snapshot
Learning Generalized Reactive Policies Using Deep Neural Networks
inferredcites →https://doi.org/10.21236/ada592141openalex-snapshot
Efficient Optimization of Control Libraries
inferredcites →https://doi.org/10.4230/lipics.itcs.2018.22openalex-snapshot
Convergence Results for Neural Networks via Electrodynamics
inferredcites →https://doi.org/10.4230/oasics.dx.2026.15openalex-snapshot
AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1502.05477openalex-snapshot
Trust Region Policy Optimization
inferredcites →https://doi.org/10.48550/arxiv.1509.02971openalex-snapshot
Continuous control with deep reinforcement learning
inferredcites →https://doi.org/10.48550/arxiv.1510.09142openalex-snapshot
Learning Continuous Control Policies by Stochastic Value Gradients
inferredcites →https://doi.org/10.48550/arxiv.1511.07289openalex-snapshot
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
inferredcites →https://doi.org/10.48550/arxiv.1604.06778openalex-snapshot
Benchmarking Deep Reinforcement Learning for Continuous Control
inferredcites →https://doi.org/10.48550/arxiv.1704.01665openalex-snapshot
Learning Combinatorial Optimization Algorithms over Graphs
inferredcites →https://doi.org/10.48550/arxiv.1707.01495openalex-snapshot
Hindsight Experience Replay
inferredcites →https://doi.org/10.48550/arxiv.1709.06560openalex-snapshot
Deep Reinforcement Learning that Matters
inferredcites →https://doi.org/10.48550/arxiv.1803.08375openalex-snapshot
Deep Learning using Rectified Linear Units (ReLU)
inferredcites →https://doi.org/10.48550/arxiv.1804.00645openalex-snapshot
Universal Planning Networks
inferredcites →https://doi.org/10.48550/arxiv.1805.08180openalex-snapshot
Hierarchical Reinforcement Learning with Hindsight
inferredcites →https://doi.org/10.48550/arxiv.1807.10366openalex-snapshot
Robot Motion Planning in Learned Latent Spaces
inferredcites →https://doi.org/10.48550/arxiv.1810.00146openalex-snapshot
Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s10514-024-10170-8openalex-snapshot
R $$\times $$ R: Rapid eXploration for Reinforcement learning via sampling-based reset distributions and imitation pre-training
inferred← citeshttps://doi.org/10.1007/s10845-021-01867-zopenalex-snapshot
A review of motion planning algorithms for intelligent robots
inferred← citeshttps://doi.org/10.1007/s41315-023-00274-2openalex-snapshot
A review of recent trend in motion planning of industrial robots
inferred← citeshttps://doi.org/10.1016/j.actaastro.2025.08.051openalex-snapshot
Rapid neural motion planning for dual space manipulators under closed-chain kinematic constraints
inferred← citeshttps://doi.org/10.1016/j.ifacol.2023.10.885openalex-snapshot
Neural motion planning in dynamic environments*
inferred← citeshttps://doi.org/10.1016/j.robot.2023.104545openalex-snapshot
Reinforcement learning in robotic motion planning by combined experience-based planning and self-imitation learning
inferred← citeshttps://doi.org/10.1049/csy2.12020openalex-snapshot
A survey of learning‐based robot motion planning
inferred← citeshttps://doi.org/10.1109/case59546.2024.10711820openalex-snapshot
Learning from Local Experience: Informed Sampling Distributions for Efficient Motion Planning
inferred← citeshttps://doi.org/10.1109/csis-iac70275.2026.11585023openalex-snapshot
Curriculum-Based Deep Reinforcement Learning for Navigation of Unmanned Ground Vehicles
inferred← citeshttps://doi.org/10.1109/icctit68197.2025.11406542openalex-snapshot
Sampling Based Path Planning Enhanced by Heterogeneous Graph Neural Networks
inferred← citeshttps://doi.org/10.1109/icra46639.2022.9812076openalex-snapshot
Learning to Retrieve Relevant Experiences for Motion Planning
inferred← citeshttps://doi.org/10.1109/icra46639.2022.9812313openalex-snapshot
Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path construction
inferred← citeshttps://doi.org/10.1109/icra48506.2021.9561104openalex-snapshot
Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions
inferred← citeshttps://doi.org/10.1109/icra48506.2021.9561119openalex-snapshot
Learning from Demonstration without Demonstrations
inferred← citeshttps://doi.org/10.1109/icra48506.2021.9561315openalex-snapshot
ReLMoGen: Integrating Motion Generation in Reinforcement Learning for Mobile Manipulation
inferred← citeshttps://doi.org/10.1109/icra48506.2021.9561411openalex-snapshot
Self-Imitation Learning by Planning
inferred← citeshttps://doi.org/10.1109/icra48506.2021.9561673openalex-snapshot
Bimanual Regrasping for Suture Needles using Reinforcement Learning for Rapid Motion Planning
inferred← citeshttps://doi.org/10.1109/icra55743.2025.11127413openalex-snapshot
Cascaded Diffusion Models for Neural Motion Planning
inferred← citeshttps://doi.org/10.1109/icra55743.2025.11127979openalex-snapshot
XMoP: Whole-Body Control Policy for Zero-Shot Cross-Embodiment Neural Motion Planning
inferred← citeshttps://doi.org/10.1109/icra55743.2025.11128856openalex-snapshot
Collision Avoidance in Model Predictive Control Using Velocity Damper
inferred← citeshttps://doi.org/10.1109/icra57147.2024.10610785openalex-snapshot
Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller
inferred← citeshttps://doi.org/10.1109/icus55513.2022.9986816openalex-snapshot
Heuristic Reward Function for Reinforcement Learning Based Manipulator Motion Planning
inferred← citeshttps://doi.org/10.1109/ijcnn48605.2020.9207427openalex-snapshot
Accelerating Reinforcement Learning for Reaching Using Continuous Curriculum Learning
inferred← citeshttps://doi.org/10.1109/iros45743.2020.9341283openalex-snapshot
Dynamically Constrained Motion Planning Networks for Non-Holonomic Robots
inferred← citeshttps://doi.org/10.1109/iros47612.2022.9981717openalex-snapshot
Speeding Up Optimization-based Motion Planning through Deep Learning
inferred← citeshttps://doi.org/10.1109/iros47612.2022.9982136openalex-snapshot
Terrain-Aware Learned Controllers for Sampling-Based Kinodynamic Planning over Physically Simulated Terrains
inferred← citeshttps://doi.org/10.1109/iros51168.2021.9636369openalex-snapshot
CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based RL
inferred← citeshttps://doi.org/10.1109/iros51168.2021.9636657openalex-snapshot
Improving Kinodynamic Planners for Vehicular Navigation with Learned Goal-Reaching Controllers
inferred← citeshttps://doi.org/10.1109/iros51168.2021.9636661openalex-snapshot
Bootstrapping Motor Skill Learning with Motion Planning
inferred← citeshttps://doi.org/10.1109/iros51168.2021.9636818openalex-snapshot
Unsupervised Path Regression Networks
inferred← citeshttps://doi.org/10.1109/iros55552.2023.10342326openalex-snapshot
DynGMP: Graph Neural Network-Based Motion Planning in Unpredictable Dynamic Environments
inferred← citeshttps://doi.org/10.1109/lra.2020.3011912openalex-snapshot
Deep Reinforcement Learning for Safe Local Planning of a Ground Vehicle in Unknown Rough Terrain
inferred← citeshttps://doi.org/10.1109/lra.2021.3116700openalex-snapshot
Sim2real Learning of Obstacle Avoidance for Robotic Manipulators in Uncertain Environments
inferred← citeshttps://doi.org/10.1109/lra.2024.3396091openalex-snapshot
Visualizing High-Dimensional Configuration Spaces: A Comprehensive Analytical Approach
inferred← citeshttps://doi.org/10.1109/nnice68970.2026.11465383openalex-snapshot
GLSPP:Geometry-Aware Latent Space Path Planning In Static Environments
inferred← citeshttps://doi.org/10.1109/tase.2022.3168542openalex-snapshot
Motion Planning of Manipulator by Points-Guided Sampling Network
inferred← citeshttps://doi.org/10.1109/tnnls.2023.3262109openalex-snapshot
Adaptive Hybrid Optimization Learning-Based Accurate Motion Planning of Multi-Joint Arm
inferred← citeshttps://doi.org/10.1109/tvt.2024.3521508openalex-snapshot
Soft Actor-Critic Combining Potential Field for Global Path Planning of Autonomous Mobile Robot
inferred← citeshttps://doi.org/10.1109/ur57808.2023.10202250openalex-snapshot
Solving Challenging Control Problems via Learning-based Motion Planning and Imitation
inferred← citeshttps://doi.org/10.1177/02783649211044405openalex-snapshot
Motion planning by learning the solution manifold in trajectory optimization
inferred← citeshttps://doi.org/10.15607/rss.2023.xix.020openalex-snapshot
Sampling-based Exploration for Reinforcement Learning of Dexterous Manipulation
inferred← citeshttps://doi.org/10.1561/2300000063openalex-snapshot
A Survey on the Integration of Machine Learning with Sampling-based Motion Planning
inferred← citeshttps://doi.org/10.1561/9781638281351openalex-snapshot
A Survey on the Integration of Machine Learning with Sampling-based Motion Planning
inferred← citeshttps://doi.org/10.33612/diss.922202894openalex-snapshot
Efficient deep reinforcement learning in robotic motion planning
inferred← citeshttps://doi.org/10.3389/frobt.2022.1030668openalex-snapshot
A data-driven approach for motion planning of industrial robots controlled by high-level motion commands
inferred← citeshttps://doi.org/10.3390/machines11070722openalex-snapshot
Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance
inferred← citeshttps://doi.org/10.3390/s25175282openalex-snapshot
Comparative Benchmark of Sampling-Based and DRL Motion Planning Methods for Industrial Robotic Arms
inferred← citeshttps://doi.org/10.34133/space.0145openalex-snapshot
Deep MARL-Based Resilient Motion Planning for Decentralized Space Manipulator
inferred← citeshttps://doi.org/10.48550/arxiv.2002.12361openalex-snapshot
Sub-Goal Trees -- a Framework for Goal-Based Reinforcement Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2008.07792openalex-snapshot
ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation
inferred← citeshttps://doi.org/10.48550/arxiv.2008.11174openalex-snapshot
Learning Obstacle Representations for Neural Motion Planning
inferred← citeshttps://doi.org/10.48550/arxiv.2011.02608openalex-snapshot
Learning a Decentralized Multi-arm Motion Planner
inferred← citeshttps://doi.org/10.48550/arxiv.2101.04736openalex-snapshot
Bootstrapping Motor Skill Learning with Motion Planning
inferred← citeshttps://doi.org/10.48550/arxiv.2102.02376openalex-snapshot
A review of motion planning algorithms for intelligent robotics
inferred← citeshttps://doi.org/10.48550/arxiv.2109.13338openalex-snapshot
Solving Challenging Control Problems Using Two-Staged Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2111.06383openalex-snapshot
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation
inferred← citeshttps://doi.org/10.48550/arxiv.2312.10918openalex-snapshot
Visualizing High-Dimensional Configuration Spaces: A Comprehensive Analytical Approach
inferred← citeshttps://doi.org/10.54097/fcis.v4i2.10198openalex-snapshot
Robot Motion Planning with Optimization-based Algorithm and Neural Network: Unsupervised Path Regression
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