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pro vyhledávání: '"How, Jonathan P"'
The reliable deployment of deep reinforcement learning in real-world settings requires the ability to generalize across a variety of conditions, including both in-distribution scenarios seen during training as well as novel out-of-distribution scenar
Externí odkaz:
http://arxiv.org/abs/2412.04323
This paper presents Robust samplE-based coVarIance StEering (REVISE), a multi-query algorithm that generates robust belief roadmaps for dynamic systems navigating through spatially dependent disturbances modeled as a Gaussian random field. Our propos
Externí odkaz:
http://arxiv.org/abs/2411.13369
Global localization is a fundamental capability required for long-term and drift-free robot navigation. However, current methods fail to relocalize when faced with significantly different viewpoints. We present ROMAN (Robust Object Map Alignment Anyw
Externí odkaz:
http://arxiv.org/abs/2410.08262
Autor:
Mahesh, Karan, Paine, Tyler M., Greene, Max L., Rober, Nicholas, Lee, Steven, Monteiro, Sildomar T., Annaswamy, Anuradha, Benjamin, Michael R., How, Jonathan P.
Marine robots must maintain precise control and ensure safety during tasks like ocean monitoring, even when encountering unpredictable disturbances that affect performance. Designing algorithms for uncrewed surface vehicles (USVs) requires accounting
Externí odkaz:
http://arxiv.org/abs/2410.01038
Autor:
Rober, Nicholas, How, Jonathan P.
Neural networks (NNs) are becoming increasingly popular in the design of control pipelines for autonomous systems. However, since the performance of NNs can degrade in the presence of out-of-distribution data or adversarial attacks, systems that have
Externí odkaz:
http://arxiv.org/abs/2410.00145
This paper presents an overview of the Burer-Monteiro method (BM), a technique that has been applied to solve robot perception problems to certifiable optimality in real-time. BM is often used to solve semidefinite programming relaxations, which can
Externí odkaz:
http://arxiv.org/abs/2410.00117
Autor:
Cai, Xiaoyi, Queeney, James, Xu, Tong, Datar, Aniket, Pan, Chenhui, Miller, Max, Flather, Ashton, Osteen, Philip R., Roy, Nicholas, Xiao, Xuesu, How, Jonathan P.
Self-supervised learning is a powerful approach for developing traversability models for off-road navigation, but these models often struggle with inputs unseen during training. Existing methods utilize techniques like evidential deep learning to qua
Externí odkaz:
http://arxiv.org/abs/2409.03005
Autor:
Aggarwal, Naman, How, Jonathan P.
The paper presents Maximal Ellipsoid Backward Reachable Trees MAXELLIPSOID BRT, which is a multi-query algorithm for planning of dynamic systems under stochastic motion uncertainty and constraints on the control input. In contrast to existing probabi
Externí odkaz:
http://arxiv.org/abs/2409.09059
Autor:
Kondo, Kota, Tewari, Claudius T., Tagliabue, Andrea, Tordesillas, Jesus, Lusk, Parker C., How, Jonathan P.
In decentralized multiagent trajectory planners, agents need to communicate and exchange their positions to generate collision-free trajectories. However, due to localization errors/uncertainties, trajectory deconfliction can fail even if trajectorie
Externí odkaz:
http://arxiv.org/abs/2406.10060
Knowing the locations of nearby moving objects is important for a mobile robot to operate safely in a dynamic environment. Dynamic object tracking performance can be improved if robots share observations of tracked objects with nearby team members in
Externí odkaz:
http://arxiv.org/abs/2405.05210