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Motion Planning

( Image credit: Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 876–900 of 920 papers

TitleStatusHype
Harnessing Reinforcement Learning for Neural Motion PlanningCode0
Track Anything Rapter(TAR)Code0
Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree searchCode0
Probabilistic Symmetry for Multi-Agent DynamicsCode0
Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State SearchCode0
Diverse Probabilistic Trajectory Forecasting with Admissibility ConstraintsCode0
Projection Mapping Implementation: Enabling Direct Externalization of Perception Results and Action Intent to Improve Robot ExplainabilityCode0
SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous RobotsCode0
LEA*: An A* Variant Algorithm with Improved Edge Efficiency for Robot Motion PlanningCode0
ViIK: Flow-based Vision Inverse Kinematics Solver with Fusing Collision CheckingCode0
Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh NetworksCode0
Direct Robot Configuration Space Construction using Convolutional Encoder-DecodersCode0
LASMP: Language Aided Subset Sampling Based Motion PlannerCode0
An Adversarial Approach to Private Flocking in Mobile Robot TeamsCode0
CoDriveVLM: VLM-Enhanced Urban Cooperative Dispatching and Motion Planning for Future Autonomous Mobility on Demand SystemsCode0
Closed-Form Minkowski Sum Approximations for Efficient Optimization-Based Collision AvoidanceCode0
Temporal and Semantic Evaluation Metrics for Foundation Models in Post-Hoc Analysis of Robotic Sub-tasksCode0
Deeply Informed Neural Sampling for Robot Motion PlanningCode0
MPTP: Motion-Planning-aware Task Planning for Navigation in Belief SpaceCode0
SLOPE: Search with Learned Optimal Pruning-based ExpansionCode0
Socially Aware Motion Planning with Deep Reinforcement LearningCode0
Language2Pose: Natural Language Grounded Pose ForecastingCode0
Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion PlannersCode0
Motion Planning NetworksCode0
Regret bounds for meta Bayesian optimization with an unknown Gaussian process priorCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GPT-DriverCollision0.35—Unverified