Zobrazeno 1 - 10
of 626
pro vyhledávání: '"Sadler, Brian M."'
Autor:
Singh, Utsav, Chakraborty, Souradip, Suttle, Wesley A., Sadler, Brian M., Namboodiri, Vinay P, Bedi, Amrit Singh
Learning control policies to perform complex robotics tasks from human preference data presents significant challenges. On the one hand, the complexity of such tasks typically requires learning policies to perform a variety of subtasks, then combinin
Externí odkaz:
http://arxiv.org/abs/2406.10892
In this work, we introduce PIPER: Primitive-Informed Preference-based Hierarchical reinforcement learning via Hindsight Relabeling, a novel approach that leverages preference-based learning to learn a reward model, and subsequently uses this reward m
Externí odkaz:
http://arxiv.org/abs/2404.13423
This paper introduces a novel non-parametric change detection algorithm to identify eclipse attacks on a blockchain network; the non-parametric algorithm relies only on the empirical mean and variance of the dataset, making it highly adaptable. An ec
Externí odkaz:
http://arxiv.org/abs/2404.00538
Autor:
Patel, Bhrij, Suttle, Wesley A., Koppel, Alec, Aggarwal, Vaneet, Sadler, Brian M., Bedi, Amrit Singh, Manocha, Dinesh
In the context of average-reward reinforcement learning, the requirement for oracle knowledge of the mixing time, a measure of the duration a Markov chain under a fixed policy needs to achieve its stationary distribution, poses a significant challeng
Externí odkaz:
http://arxiv.org/abs/2403.11925
Autor:
Suttle, Wesley A., Sharma, Vipul K., Kosaraju, Krishna C., Sivaranjani, S., Liu, Ji, Gupta, Vijay, Sadler, Brian M.
We develop provably safe and convergent reinforcement learning (RL) algorithms for control of nonlinear dynamical systems, bridging the gap between the hard safety guarantees of control theory and the convergence guarantees of RL theory. Recent advan
Externí odkaz:
http://arxiv.org/abs/2403.04007
Autor:
Wu, Xiyang, Chakraborty, Souradip, Xian, Ruiqi, Liang, Jing, Guan, Tianrui, Liu, Fuxiao, Sadler, Brian M., Manocha, Dinesh, Bedi, Amrit Singh
In this paper, we highlight the critical issues of robustness and safety associated with integrating large language models (LLMs) and vision-language models (VLMs) into robotics applications. Recent works focus on using LLMs and VLMs to improve the p
Externí odkaz:
http://arxiv.org/abs/2402.10340
Deceptive path planning (DPP) is the problem of designing a path that hides its true goal from an outside observer. Existing methods for DPP rely on unrealistic assumptions, such as global state observability and perfect model knowledge, and are typi
Externí odkaz:
http://arxiv.org/abs/2402.06552
Integrated sensing and communications (ISAC) systems have gained significant interest because of their ability to jointly and efficiently access, utilize, and manage the scarce electromagnetic spectrum. The co-existence approach toward ISAC focuses o
Externí odkaz:
http://arxiv.org/abs/2310.14167
Hypercomplex signal processing (HSP) provides state-of-the-art tools to handle multidimensional signals by harnessing intrinsic correlation of the signal dimensions through Clifford algebra. Recently, the hypercomplex representation of the phase retr
Externí odkaz:
http://arxiv.org/abs/2310.17660
Higher spectral and energy efficiencies are the envisioned defining characteristics of high data-rate sixth-generation (6G) wireless networks. One of the enabling technologies to meet these requirements is index modulation (IM), which transmits infor
Externí odkaz:
http://arxiv.org/abs/2310.03208