2 citations · 4 across the 7 of their papers we have counts for
8 papers
Information-theoretic receding-horizon active learning of nonlinear dynamical systems
Juncal Arbelaiz, Anushri Arora, Jonathan W. Pillow
Accurately learning nonlinear dynamics from a finite-duration experiment requires the efficient collection of informative data. We address this challenge for stochastic controlled…
The role of communication delays in the optimal control of spatially invariant systems
Luca Ballotta, Juncal Arbelaiz, Vijay Gupta +2
We study optimal proportional feedback controllers for spatially invariant systems when the controller has access to delayed state measurements received from different spatial loca…
Spiking control systems for soft robotics: a rhythmic case study in a soft robotic crawler
Juncal Arbelaiz, Alessio Franci, Naomi Ehrich Leonard +2
Inspired by spiking neural feedback, we propose a spiking controller to engineer the locomotion of a soft robotic crawler. Its bistability, akin to neural fast positive feedback, c…
Inferring System and Optimal Control Parameters of Closed-Loop Systems from Partial Observations
Victor Geadah, Juncal Arbelaiz, Harrison Ritz +3
We consider the joint problem of system identification and inverse optimal control for discrete-time stochastic Linear Quadratic Regulators. We analyze finite and infinite time hor…
Optimal gait design for nonlinear soft robotic crawlers
Yenan Shen, Naomi Ehrich Leonard, Bassam Bamieh +1
Soft robots offer a frontier in robotics with enormous potential for safe human-robot interaction and agility in uncertain environments. A stepping stone towards unlocking their po…
Optimal estimation in spatially distributed systems: how far to share measurements from?
Juncal Arbelaiz, Bassam Bamieh, Anette E. Hosoi +1
We consider the centralized optimal estimation problem in spatially distributed systems. We use the setting of spatially invariant systems as an idealization for which concrete and…
Excitable crawling
Juncal Arbelaiz, Alessio Franci, Naomi Ehrich Leonard +2
We propose and analyze the suitability of a spiking controller to engineer the locomotion of a soft robotic crawler. Inspired by the FitzHugh-Nagumo model of neural excitability, w…
Personalized Cancer Chemotherapy Schedule: a numerical comparison of performance and robustness in model-based and model-free scheduling methodologies
Jesus Tordesillas, Juncal Arbelaiz
Reinforcement learning algorithms are gaining popularity in fields in which optimal scheduling is important, and oncology is not an exception. The complex and uncertain dynamics of…