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20172021
most citedBenchmark Environments for Multitask Learning in Continuous Domains

17 citations · 28 across the 9 of their papers we have counts for

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10 papers · 1 filter

cs.RO2021

An Autonomous Probing System for Collecting Measurements at Depth from Small Surface Vehicles

Yuying Huang, Yiming Yao, Johanna Hansen +4

This paper presents the portable autonomous probing system (APS), a low-cost robotic design for collecting water quality measurements at targeted depths from an autonomous surface…

cs.RO2020

Seeing Through your Skin: Recognizing Objects with a Novel Visuotactile Sensor

Francois Robert Hogan, Michael Jenkin, Sahand Rezaei-Shoshtari +3

We introduce a new class of vision-based sensor and associated algorithmic processes that combine visual imaging with high-resolution tactile sending, all in a uniform hardware and…

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

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…

cs.RO20195 cited

Heterogeneous Robot Teams for Informative Sampling

Travis Manderson, Sandeep Manjanna, Gregory Dudek

In this paper we present a cooperative multi-robot strategy to adaptively explore and sample environments that are unfavorable for humans. We propose a methodology for a team of he…

cs.RO20192 cited

Reinforcement Learning with Non-uniform State Representations for Adaptive Search

Sandeep Manjanna, Herke van Hoof, Gregory Dudek

Efficient spatial exploration is a key aspect of search and rescue. In this paper, we present a search algorithm that generates efficient trajectories that optimize the rate at whi…