Motion Planning
251 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
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
maudzung/Complex-YOLOv4-Pytorch
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We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only.
Learning Latent Dynamics for Planning from Pixels
Planning has been very successful for control tasks with known environment dynamics.
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
mfe7/cadrl_ros
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This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules.
STRIPS Planning in Infinite Domains
We introduce STRIPStream: an extension of the STRIPS language which can model these domains by supporting the specification of blackbox generators to handle complex constraints.
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning
We extend PDDL to support a generic, declarative specification for these procedures that treats their implementation as black boxes.
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
daeheepark/PathPredictNusc
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Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact.
Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach
astrohiro/ncm
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This paper presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric (NCM).
Formation Control for Connected and Automated Vehicles on Multi-lane Roads: Relative Motion Planning and Conflict Resolution
Multi-vehicle coordinated decision making and control can improve traffic efficiency while guaranteeing driving safety.
SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planning
Most RL agents exhibit a limited capacity to generalize, often focusing narrowly on specific scenarios, and are usually evaluated in similar or even the same scenarios seen during training.
One Thousand and One Hours: Self-driving Motion Prediction Dataset
wenkaip-1836890/pyromid_l5prediction
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Motivated by the impact of large-scale datasets on ML systems we present the largest self-driving dataset for motion prediction to date, containing over 1, 000 hours of data.
