most citedBenchmark Environments for Multitask Learning in Continuous Domains

17 citations · 23 across the 3 of their papers we have counts for

collaborators

5 papers

cs.RO20206 cited

Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models

Yuchen Wu, Melissa Mozifian, Florian Shkurti

The potential benefits of model-free reinforcement learning to real robotics systems are limited by its uninformed exploration that leads to slow convergence, lack of data-efficien…

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

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.RO2017

Underwater Multi-Robot Convoying using Visual Tracking by Detection

Florian Shkurti, Wei-Di Chang, Peter Henderson +7

We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments. Our method…

cs.AI201717 cited

Benchmark Environments for Multitask Learning in Continuous Domains

Peter Henderson, Wei-Di Chang, Florian Shkurti +3

As demand drives systems to generalize to various domains and problems, the study of multitask, transfer and lifelong learning has become an increasingly important pursuit. In disc…