2 citations · 3 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…
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.…