Motion Planning
196 papers with code • 1 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Motion Planning
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Libraries
Use these libraries to find Motion Planning models and implementations
nuScenes
HouseExpo
ThreeDWorld Transport Challenge
PushWorld
Most implemented papers
MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs
westny/mtp-go
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Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior.
Scene as Occupancy
opendrivelab/occnet
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ICCV 2023
Human driver can easily describe the complex traffic scene by visual system.
Parting with Misconceptions about Learning-based Vehicle Motion Planning
autonomousvision/tuplan_garage
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The release of nuPlan marks a new era in vehicle motion planning research, offering the first large-scale real-world dataset and evaluation schemes requiring both precise short-term planning and long-horizon ego-forecasting.
Neural Potential Field for Obstacle-Aware Local Motion Planning
Experiment on Husky UGV mobile robot showed that our approach allows real-time and safe local planning.
Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?
We assess existing state-of-the-art planners on our benchmark and show that neither rule-based nor learning-based planners can safely navigate the interPlan scenarios.
CAD2RL: Real Single-Image Flight without a Single Real Image
We propose a learning method that we call CAD$^2$RL, which can be used to perform collision-free indoor flight in the real world while being trained entirely on 3D CAD models.
Landmark Guided Probabilistic Roadmap Queries
A landmark based heuristic is investigated for reducing query phase run-time of the probabilistic roadmap (\PRM) motion planning method.
Learning Heuristic Search via Imitation
In this paper, we do so by training a heuristic policy that maps the partial information from the search to decide which node of the search tree to expand.
Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics
This hierarchical planning approach results in a decomposition of the POMDP planning problem into smaller sub-parts that can be solved with significantly lower computational costs.
Motion Planning Networks
ahq1993/MPNet
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Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars.
