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20172022
most citedData-driven Koopman Operators for Model-based Shared Control of Human-Machine Systems

37 citations · 58 across the 9 of their papers we have counts for

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12 papers · 1 filter

cs.RO2021

Learning to Control Complex Robots Using High-Dimensional Interfaces: Preliminary Insights

Jongmin M. Lee, Temesgen Gebrekristos, Dalia De Santis +5

Human body motions can be captured as a high-dimensional continuous signal using motion sensor technologies. The resulting data can be surprisingly rich in information, even when c…

cs.RO2021

An Analysis of Human-Robot Information Streams to Inform Dynamic Autonomy Allocation

Christopher X. Miller, Temesgen Gebrekristos, Michael Young +2

A dynamic autonomy allocation framework automatically shifts how much control lies with the human versus the robotics autonomy, for example based on factors such as environmental s…

cs.RO20207 cited

Characterization of Assistive Robot Arm Teleoperation: A Preliminary Study to Inform Shared Control

Mahdieh Nejati Javaremi, Brenna D. Argall

Assistive robotic devices can increase the independence of individuals with motor impairments. However, each person is unique in their level of injury, preferences, and skills, whi…

cs.RO2020

Customized Handling of Unintended Interface Operation in Assistive Robots

Deepak Gopinath, Mahdieh Nejati Javaremi, Brenna D. Argall

We present an assistance system that reasons about a human's intended actions during robot teleoperation in order to provide appropriate corrections for unintended behavior. We mod…

cs.RO202037 cited

Data-driven Koopman Operators for Model-based Shared Control of Human-Machine Systems

Alexander Broad, Ian Abraham, Todd Murphey +1

We present a data-driven shared control algorithm that can be used to improve a human operator's control of complex dynamic machines and achieve tasks that would otherwise be chall…

cs.RO2020

Hybrid Control for Learning Motor Skills

Ian Abraham, Alexander Broad, Allison Pinosky +2

We develop a hybrid control approach for robot learning based on combining learned predictive models with experience-based state-action policy mappings to improve the learning capa…