1 citations · 1 across the 10 of their papers we have counts for
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Beyond Topology: A Morphological Symmetry Graph Representation for Locomotion Policy Learning
Sizhe Wei, Xulin Chen, Fengze Xie +3
Reinforcement learning has enabled impressive locomotion skills on articulated robots, but common policy representations remain only weakly aligned with robot physics. Generic netw…
A Narwhal-Inspired Sensing-to-Control Framework for Small Fixed-Wing Aircraft
Fengze Xie, Xiaozhou Fan, Jacob Schuster +2
Fixed-wing unmanned aerial vehicles (UAVs) offer endurance and efficiency but lack low-speed agility due to highly coupled dynamics. We present an end-to-end sensing-to-control pip…
Fast Non-Episodic Adaptive Tuning of Robot Controllers with Online Policy Optimization
James A. Preiss, Fengze Xie, Yiheng Lin +2
We study online algorithms to tune the parameters of a robot controller in a setting where the dynamics, policy class, and optimality objective are all time-varying. The system fol…
Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning
Fengze Xie, Sizhe Wei, Yue Song +2
We present a morphological-symmetry-equivariant heterogeneous graph neural network, namely MS-HGNN, for robotic dynamics learning, that integrates robotic kinematic structures and…
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.…