activity
20172023
most citedBenchmark Environments for Multitask Learning in Continuous Domains

17 citations · 30 across the 7 of their papers we have counts for

collaborators

7 papers

cs.RO2023

Learning active tactile perception through belief-space control

Jean-François Tremblay, David Meger, Francois Hogan +1

Robots operating in an open world will encounter novel objects with unknown physical properties, such as mass, friction, or size. These robots will need to sense these properties t…

cs.AI2023★ 2 cited

Imitation from Observation With Bootstrapped Contrastive Learning

Medric Sonwa, Johanna Hansen, Eugene Belilovsky

Imitation from observation (IfO) is a learning paradigm that consists of training autonomous agents in a Markov Decision Process (MDP) by observing expert demonstrations without ac…

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.RO2021

Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching

Tu-Hoa Pham, William Seto, Shreyansh Daftry +11

We consider the problem of rover relocalization in the context of the notional Mars Sample Return campaign. In this campaign, a rover (R1) needs to be capable of autonomously navig…

cs.RO2018★ 11 cited

Autonomous Marine Sampling Enhanced by Strategically Deployed Drifters in Marine Flow Fields

Johanna Hansen, Sandeep Manjanna, Alberto Quattrini Li +2

We present a transportable system for ocean observations in which a small autonomous surface vehicle (ASV) adaptively collects spatially diverse samples with aid from a team of ine…

cs.LG2018

Planning in Dynamic Environments with Conditional Autoregressive Models

Johanna Hansen, Kyle Kastner, Aaron Courville +1

We demonstrate the use of conditional autoregressive generative models (van den Oord et al., 2016a) over a discrete latent space (van den Oord et al., 2017b) for forward planning w…

cs.AI2017★ 17 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…