Search Results for author:
Found 13 papers, 6 papers with code
Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty
An agent can use domain knowledge provided by human experts to learn efficiently.
Fast Design Space Exploration of Nonlinear Systems: Part I
This paper presents a new method of solving the inverse design problem namely, given requirements or constraints on output, find an input that also optimizes an objective function.
Robust Place Recognition using an Imaging Lidar
We propose a methodology for robust, real-time place recognition using an imaging lidar, which yields image-quality high-resolution 3D point clouds.
Robot Design With Neural Networks, MILP Solvers and Active Learning
Central to the design of many robot systems and their controllers is solving a constrained blackbox optimization problem.
Variational Filtering with Copula Models for SLAM
The ability to infer map variables and estimate pose is crucial to the operation of autonomous mobile robots.
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs
We consider an autonomous exploration problem in which a range-sensing mobile robot is tasked with accurately mapping the landmarks in an a priori unknown environment efficiently in real-time; it must choose sensing actions that both curb localization uncertainty and achieve information gain.
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
We propose a framework for tightly-coupled lidar inertial odometry via smoothing and mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory estimation and map-building.
Robotics
Simulation-based Lidar Super-resolution for Ground Vehicles
We propose a methodology for lidar super-resolution with ground vehicles driving on roadways, which relies completely on a driving simulator to enhance, via deep learning, the apparent resolution of a physical lidar.
Robotics Image and Video Processing
Stochastically Dominant Distributional Reinforcement Learning
We describe a new approach for managing aleatoric uncertainty in the Reinforcement Learning (RL) paradigm.
Distributional Reinforcement Learning
reinforcement-learning
+1
Self-Learning Exploration and Mapping for Mobile Robots via Deep Reinforcement Learning
Mapping and exploration of a priori unknown environments is a crucial capability for mobile robot autonomy.
Recursive Sparse Pseudo-input Gaussian Process SARSA
The class of Gaussian Process (GP) methods for Temporal Difference learning has shown promise for data-efficient model-free Reinforcement Learning.
Sparse Gaussian Process Temporal Difference Learning for Marine Robot Navigation
Our results show SPGP-SARSA can outperform the state-of-the-art sparse method, replicate the prediction quality of its exact counterpart, and be applied to solve underwater navigation tasks.
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain
We propose a lightweight and ground-optimized lidar odometry and mapping method, LeGO-LOAM, for realtime six degree-of-freedom pose estimation with ground vehicles.
