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
Latest papers
Optimizing Crowd-Aware Multi-Agent Path Finding through Local Broadcasting with Graph Neural Networks
Multi-Agent Path Finding (MAPF) in crowded environments presents a challenging problem in motion planning, aiming to find collision-free paths for all agents in the system.
LEA*: An A* Variant Algorithm with Improved Edge Efficiency for Robot Motion Planning
LEA* is simple and easy to implement with minimum modification to A*, resulting in a very small overhead compared to previous lazy search algorithms.
A Unifying Variational Framework for Gaussian Process Motion Planning
luke-ck/vgpmp
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To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints, generating smooth and stable motions, avoiding obstacles, and preventing collisions.
3DMOTFormer: Graph Transformer for Online 3D Multi-Object Tracking
dsx0511/3dmotformer
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ICCV 2023
Tracking 3D objects accurately and consistently is crucial for autonomous vehicles, enabling more reliable downstream tasks such as trajectory prediction and motion planning.
SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Replicable Scenes
We present a new reproducible benchmark for evaluating robot manipulation in the real world, specifically focusing on pick-and-place.
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.
AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers
Rather than using LLMs to directly plan task sub-goals, we instead perform few-shot translation from natural language task descriptions to an intermediate task representation that can then be consumed by a TAMP algorithm to jointly solve the task and motion plan.
iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning
Our approach for intent-aware planning, iPLAN, allows agents to infer nearby drivers' intents solely from their local observations.
Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging scenarios
In addition, we study the impact of the proposed prediction approach on motion planning and control tasks using extensive merging scenarios from the exiD dataset.
Scene as Occupancy
opendrivelab/occnet
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ICCV 2023
Human driver can easily describe the complex traffic scene by visual system.
