2 citations · 2 across the 11 of their papers we have counts for
12 papers
Wind-Informed Rapid Flight-Planning in Complex Urban Topologies via Machine Learning and Experimental Validation
Peter I. Renn, Alejandro A. Stefan-Zavala, Julian Humml +8
Advanced air mobility operations hold the potential to enhance and expand regional transportation of both people and goods in populated areas. However, hazardous flight conditions…
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…
Conformal Risk Training: End-to-End Optimization of Conformal Risk Control
Christopher Yeh, Nicolas Christianson, Adam Wierman +1
While deep learning models often achieve high predictive accuracy, their predictions typically do not come with any provable guarantees on risk or reliability, which are critical f…
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…
End-to-End Conformal Calibration for Optimization Under Uncertainty
Christopher Yeh, Nicolas Christianson, Alan Wu +2
Machine learning can significantly improve performance for decision-making under uncertainty across a wide range of domains. However, ensuring robustness guarantees requires well-c…
Distributionally Robust Constrained Reinforcement Learning under Strong Duality
Zhengfei Zhang, Kishan Panaganti, Laixi Shi +3
We study the problem of Distributionally Robust Constrained RL (DRC-RL), where the goal is to maximize the expected reward subject to environmental distribution shifts and constrai…
Population Transformer: Learning Population-level Representations of Neural Activity
Geeling Chau, Christopher Wang, Sabera Talukder +5
We present a self-supervised framework that learns population-level codes for arbitrary ensembles of neural recordings at scale. We address key challenges in scaling models with ne…
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.…