Motion Planning
72 papers with code • 0 benchmarks • 2 datasets
Benchmarks
HouseExpo
ThreeDWorld Transport Challenge
Greatest papers with code
Baidu Apollo EM Motion Planner
In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform.
Learning Latent Dynamics for Planning from Pixels
Planning has been very successful for control tasks with known environment dynamics.
Ranked #2 on
Continuous Control
on DeepMind Cheetah Run (Images)
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.
One Thousand and One Hours: Self-driving Motion Prediction Dataset
lyft/l5kit
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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.
FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments
mit-acl/faster
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The standard approach to ensure safety is to enforce a "stop" condition in the free-known space.
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.
miniSAM: A Flexible Factor Graph Non-linear Least Squares Optimization Framework
Many problems in computer vision and robotics can be phrased as non-linear least squares optimization problems represented by factor graphs, for example, simultaneous localization and mapping (SLAM), structure from motion (SfM), motion planning, and control.
3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams
In this paper, we focus on obtaining 2D and 3D labels, as well as track IDs for objects on the road with the help of a novel 3D Bounding Box Annotation Toolbox (3D BAT).
Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners
ahq1993/MPNet
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We validate MPNet against gold-standard and state-of-the-art planning methods in a variety of problems from 2D to 7D robot configuration spaces in challenging and cluttered environments, with results showing significant and consistently stronger performance metrics, and motivating neural planning in general as a modern strategy for solving motion planning problems efficiently.
Deeply Informed Neural Sampling for Robot Motion Planning
ahq1993/MPNet
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In this paper, we present a neural network-based adaptive sampler for motion planning called Deep Sampling-based Motion Planner (DeepSMP).