Motion Planning
( Image credit: Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning )
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Greatest papers with code
Learning Latent Dynamics for Planning from Pixels
Planning has been very successful for control tasks with known environment dynamics.
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only.
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).
Deeply Informed Neural Sampling for Robot Motion Planning
• ahq1993/MPNet
• 
In this paper, we present a neural network-based adaptive sampler for motion planning called Deep Sampling-based Motion Planner (DeepSMP).
Motion Planning Networks
• ahq1993/MPNet
•
Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars.
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.
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.
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving.