2 citations · 3 across the 8 of their papers we have counts for
8 papers
Sampling-Based Coordination-Informed Multi-Objective Multi-Robot Reinforcement Learning
Antonio Marino, Esteban Restrepo, Soon-jo Chung +2
Multi-robot systems must simultaneously optimize competing objectives while maintaining coordinated behavior. Existing multi-agent reinforcement learning approaches often rely on f…
Planetary Exploration 3.0: A Roadmap for Software-Defined, Radically Adaptive Space Systems
Masahiro Ono, Daniel Selva, Morgan L. Cable +23
The surface and subsurface of worlds beyond Mars remain largely unexplored. Yet these worlds hold keys to fundamental questions in planetary science - from potentially habitable su…
Meta-Learning Augmented MPC for Disturbance-Aware Motion Planning and Control of Quadrotors
Dženan Lapandić, Fengze Xie, Christos K. Verginis +3
A major challenge in autonomous flights is unknown disturbances, which can jeopardize safety and lead to collisions, especially in obstacle-rich environments. This paper presents a…
MAGIC-VFM: Meta-learning Adaptation for Ground Interaction Control with Visual Foundation Models
Elena Sorina Lupu, Fengze Xie, James A. Preiss +3
Control of off-road vehicles is challenging due to the complex dynamic interactions with the terrain. Accurate modeling of these interactions is important to optimize driving perfo…
Online Policy Optimization in Unknown Nonlinear Systems
Yiheng Lin, James A. Preiss, Fengze Xie +4
We study online policy optimization in nonlinear time-varying dynamical systems where the true dynamical models are unknown to the controller. This problem is challenging because,…
Generalizability Under Sensor Failure: Tokenization + Transformers Enable More Robust Latent Spaces
Geeling Chau, Yujin An, Ahamed Raffey Iqbal +3
A major goal in neuroscience is to discover neural data representations that generalize. This goal is challenged by variability along recording sessions (e.g. environment), subject…
Hierarchical Meta-learning-based Adaptive Controller
Fengze Xie, Guanya Shi, Michael O'Connell +2
We study how to design learning-based adaptive controllers that enable fast and accurate online adaptation in changing environments. In these settings, learning is typically done d…
Joint-Space Multi-Robot Motion Planning with Learned Decentralized Heuristics
Fengze Xie, Marcus Dominguez-Kuhne, Benjamin Riviere +4
In this paper, we present a method of multi-robot motion planning by biasing centralized, sampling-based tree search with decentralized, data-driven steer and distance heuristics.…