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20172024
most citedHeterogeneous Robot Teams for Informative Sampling

5 citations · 6 across the 5 of their papers we have counts for

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Showing 2020 · cs.ROShow all

5 papers · 2 filters

cs.RO2020

Multimodal dynamics modeling for off-road autonomous vehicles

Jean-François Tremblay, Travis Manderson, Aurélio Noca +2

Dynamics modeling in outdoor and unstructured environments is difficult because different elements in the environment interact with the robot in ways that can be hard to predict. L…

cs.RO2020

Vision-Based Goal-Conditioned Policies for Underwater Navigation in the Presence of Obstacles

Travis Manderson, Juan Camilo Gamboa Higuera, Stefan Wapnick +4

We present Nav2Goal, a data-efficient and end-to-end learning method for goal-conditioned visual navigation. Our technique is used to train a navigation policy that enables a robot…

cs.RO2020

Learning to Drive Off Road on Smooth Terrain in Unstructured Environments Using an On-Board Camera and Sparse Aerial Images

Travis Manderson, Stefan Wapnick, David Meger +1

We present a method for learning to drive on smooth terrain while simultaneously avoiding collisions in challenging off-road and unstructured outdoor environments using only visual…

cs.RO2020

One-Shot Informed Robotic Visual Search in the Wild

Karim Koreitem, Florian Shkurti, Travis Manderson +3

We consider the task of underwater robot navigation for the purpose of collecting scientifically relevant video data for environmental monitoring. The majority of field robots that…

cs.RO2020

DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization

Bharat Joshi, Md Modasshir, Travis Manderson +5

In this paper, we propose a real-time deep learning approach for determining the 6D relative pose of Autonomous Underwater Vehicles (AUV) from a single image. A team of autonomous…