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.
3D OBJECT DETECTION AUTONOMOUS DRIVING MOTION PLANNING REGRESSION
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.
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.
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
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
• MoonBlvd/tad-IROS2019-TBD-
•
Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving.
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).