Motion Planning
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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
• 
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.
STRIPStream: Integrating Symbolic Planners and Blackbox Samplers
Many planning applications involve complex relationships defined on high-dimensional, continuous variables.
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.
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
NeurIPS 2018 • beomjoonkim/MetaLearnBO
Bayesian optimization usually assumes that a Bayesian prior is given.