129 citations · 889 across the 54 of their papers we have counts for
15 papers · 2 filters
Dynamical System Segmentation for Information Measures in Motion
Thomas A. Berrueta, Ana Pervan, Kathleen Fitzsimons +1
Motions carry information about the underlying task being executed. Previous work in human motion analysis suggests that complex motions may result from the composition of fundamen…
Learning from Human Directional Corrections
Wanxin Jin, Todd D. Murphey, Zehui Lu +1
This paper proposes a novel approach that enables a robot to learn an objective function incrementally from human directional corrections. Existing methods learn from human magnitu…
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…
Learning from Sparse Demonstrations
Wanxin Jin, Todd D. Murphey, Dana Kulić +2
This paper develops the method of Continuous Pontryagin Differentiable Programming (Continuous PDP), which enables a robot to learn an objective function from a few sparsely demons…
Information Requirements of Collision-Based Micromanipulation
Alexandra Q. Nilles, Ana Pervan, Thomas A. Berrueta +2
We present a task-centered formal analysis of the relative power of several robot designs, inspired by the unique properties and constraints of micro-scale robotic systems. Our tas…
Formalizing and Guaranteeing* Human-Robot Interaction
Hadas Kress-Gazit, Kerstin Eder, Guy Hoffman +12
Robot capabilities are maturing across domains, from self-driving cars, to bipeds and drones. As a result, robots will soon no longer be confined to safety-controlled industrial se…
Algorithmic Design for Embodied Intelligence in Synthetic Cells
Ana Pervan, Todd D. Murphey
In nature, biological organisms jointly evolve both their morphology and their neurological capabilities to improve their chances for survival. Consequently, task information is en…
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…
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…
Shoulder abduction loading affects motor coordination in individuals with chronic stroke, informing targeted rehabilitation
Aleksandra Kalinowska, Kyra Rudy, Millicent Schlafly +3
Individuals post stroke experience motor impairments, such as loss of independent joint control, leading to an overall reduction in arm function. Their motion becomes slower and mo…
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…
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…
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…
Learning Stable Models for Prediction and Control
Giorgos Mamakoukas, Ian Abraham, Todd D. Murphey
This paper demonstrates the benefits of imposing stability on data-driven Koopman operators. The data-driven identification of stable Koopman operators (DISKO) is implemented using…
Bayesian Particles on Cyclic Graphs
Ana Pervan, Todd Murphey
We consider the problem of designing synthetic cells to achieve a complex goal (e.g., mimicking the immune system by seeking invaders) in a complex environment (e.g., the circulato…