Motion Planning
154 papers with code • 0 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Motion Planning
Libraries
Use these libraries to find Motion Planning models and implementations
HouseExpo
ThreeDWorld Transport Challenge
PushWorld
Most implemented papers
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
maudzung/Complex-YOLOv4-Pytorch
•
•
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
•
•
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.
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.
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.
Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach
astrohiro/ncm
•
•
This paper presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric (NCM).
One Thousand and One Hours: Self-driving Motion Prediction Dataset
wenkaip-1836890/pyromid_l5prediction
•
•
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.
Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces
The new monotone solver is integrated with a global planner to solve non-monotone instances with high-quality solutions fast.
Socially Aware Motion Planning with Deep Reinforcement Learning
mit-acl/gym-collision-avoidance
•
•
For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e. g., passing on the right).
