5 citations · 6 across the 7 of their papers we have counts for
8 papers
Ergodic Control and Controlled Diffusion for Robot Learning: Review and Tutorial
Max Muchen Sun, Cem Bilaloglu, Ananya Rao +6
Diffusion learning leverages the statistical mechanism of diffusion processes for learning, reasoning, and inferring complex distributions from data. Recent advances in diffusion l…
Flow Matching Ergodic Coverage
Max Muchen Sun, Allison Pinosky, Todd Murphey
Ergodic coverage effectively generates exploratory behaviors for embodied agents by aligning the spatial distribution of the agent's trajectory with a target distribution, where th…
Inverse Mixed Strategy Games with Generative Trajectory Models
Max Muchen Sun, Pete Trautman, Todd Murphey
Game-theoretic models are effective tools for modeling multi-agent interactions, especially when robots need to coordinate with humans. However, applying these models requires infe…
Koopman Operators in Robot Learning
Lu Shi, Masih Haseli, Giorgos Mamakoukas +5
Koopman operator theory offers a rigorous treatment of dynamics and has been emerging as an alternative modeling and learning-based control method across various robotics sub-domai…
Human Robot Pacing Mismatch
Muchen Sun, Peter Trautman, Todd Murphey
A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside human is that the crowd density exceeds a threshold such that all feasible t…
Mixed Strategy Nash Equilibrium for Crowd Navigation
Max Muchen Sun, Francesca Baldini, Katie Hughes +2
Robots navigating in crowded areas should negotiate free space with humans rather than fully controlling collision avoidance, as this can lead to freezing behavior. Game theory pro…
Fast Ergodic Search with Kernel Functions
Max Muchen Sun, Ayush Gaggar, Peter Trautman +1
Ergodic search enables optimal exploration of an information distribution while guaranteeing the asymptotic coverage of the search space. However, current methods typically have ex…
Derivative-Based Koopman Operators for Real-Time Control of Robotic Systems
Giorgos Mamakoukas, Maria L. Castano, Xiaobo Tan +1
This paper presents a generalizable methodology for data-driven identification of nonlinear dynamics that bounds the model error in terms of the prediction horizon and the magnitud…