37 citations · 37 across the 3 of their papers we have counts for
7 papers · 1 filter
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…
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…
Highly Parallelized Data-driven MPC for Minimal Intervention Shared Control
Alexander Broad, Todd Murphey, Brenna Argall
We present a shared control paradigm that improves a user's ability to operate complex, dynamic systems in potentially dangerous environments without a priori knowledge of the user…
Operation and Imitation under Safety-Aware Shared Control
Alexander Broad, Todd Murphey, Brenna Argall
We describe a shared control methodology that can, without knowledge of the task, be used to improve a human's control of a dynamic system, be used as a training mechanism, and be…
Learning Models for Shared Control of Human-Machine Systems with Unknown Dynamics
Alexander Broad, Todd Murphey, Brenna Argall
We present a novel approach to shared control of human-machine systems. Our method assumes no a priori knowledge of the system dynamics. Instead, we learn both the dynamics and inf…
Structured Neural Network Dynamics for Model-based Control
Alexander Broad, Ian Abraham, Todd Murphey +1
We present a structured neural network architecture that is inspired by linear time-varying dynamical systems. The network is designed to mimic the properties of linear dynamical s…