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20172022
most citedModel-Based Control Using Koopman Operators

85 citations · 186 across the 8 of their papers we have counts for

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13 papers · 1 filter

cs.RO2020

Dynamics and Domain Randomized Gait Modulation with Bezier Curves for Sim-to-Real Legged Locomotion

Maurice Rahme, Ian Abraham, Matthew L. Elwin +1

We present a sim-to-real framework that uses dynamics and domain randomized offline reinforcement learning to enhance open-loop gaits for legged robots, allowing them to traverse u…

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.RO20202 cited

Ergodic Specifications for Flexible Swarm Control: From User Commands to Persistent Adaptation

Ahalya Prabhakar, Ian Abraham, Annalisa Taylor +7

This paper presents a formulation for swarm control and high-level task planning that is dynamically responsive to user commands and adaptable to environmental changes. We design a…

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…

cs.RO202032 cited

Model-Based Generalization Under Parameter Uncertainty Using Path Integral Control

Ian Abraham, Ankur Handa, Nathan Ratliff +3

This work addresses the problem of robot interaction in complex environments where online control and adaptation is necessary. By expanding the sample space in the free energy form…

cs.RO2020

An Ergodic Measure for Active Learning From Equilibrium

Ian Abraham, Ahalya Prabhakar, Todd D. Murphey

This paper develops KL-Ergodic Exploration from Equilibrium (), a method for robotic systems to integrate stability into actively generating informative measurements…