129 citations · 889 across the 54 of their papers we have counts for
45 papers · 1 filter
Automated Gait Generation For Walking, Soft Robotic Quadrupeds
Jake Ketchum, Sophia Schiffer, Muchen Sun +3
Gait generation for soft robots is challenging due to the nonlinear dynamics and high dimensional input spaces of soft actuators. Limitations in soft robotic control and perception…
Scale-Invariant Specifications for Human-Swarm Systems
Joel Meyer, Ahalya Prabhakar, Allison Pinosky +9
We present a method for controlling a swarm using its spectral decomposition -- that is, by describing the set of trajectories of a swarm in terms of a spatial distribution through…
A Game Benchmark for Real-Time Human-Swarm Control
Joel Meyer, Allison Pinosky, Thomas Trzpit +2
We present a game benchmark for testing human-swarm control algorithms and interfaces in a real-time, high-cadence scenario. Our benchmark consists of a swarm vs. swarm game in a v…
Majorization Minimization Methods for Distributed Pose Graph Optimization
Taosha Fan, Todd Murphey
We consider the problem of distributed pose graph optimization (PGO) that has important applications in multi-robot simultaneous localization and mapping (SLAM). We propose the maj…
Active Learning in Robotics: A Review of Control Principles
Annalisa T. Taylor, Thomas A. Berrueta, Todd D. Murphey
Active learning is a decision-making process. In both abstract and physical settings, active learning demands both analysis and action. This is a review of active learning in robot…
Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation
Muchen Sun, Francesca Baldini, Peter Trautman +1
Cooperatively avoiding collision is a critical functionality for robots navigating in dense human crowds, failure of which could lead to either overaggressive or overcautious behav…
Ergodic imitation: Learning from what to do and what not to do
Aleksandra Kalinowska, Ahalya Prabhakar, Kathleen Fitzsimons +1
With growing access to versatile robotics, it is beneficial for end users to be able to teach robots tasks without needing to code a control policy. One possibility is to teach the…
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…
Task-Based Hybrid Shared Control for Training Through Forceful Interaction
Kathleen Fitzsimons, Aleksandra Kalinowska, Julius P. A. Dewald +1
Despite the fact that robotic platforms can provide both consistent practice and objective assessments of users over the course of their training, there are relatively few instance…
Active Learning of Dynamics for Data-Driven Control Using Koopman Operators
Ian Abraham, Todd D. Murphey
This paper presents an active learning strategy for robotic systems that takes into account task information, enables fast learning, and allows control to be readily synthesized by…