Zobrazeno 1 - 10
of 326
pro vyhledávání: '"SASTRY, S. SHANKAR"'
Autor:
Chen, Zixuan, He, Xialin, Wang, Yen-Jen, Liao, Qiayuan, Ze, Yanjie, Li, Zhongyu, Sastry, S. Shankar, Wu, Jiajun, Sreenath, Koushil, Gupta, Saurabh, Peng, Xue Bin
Reinforcement learning combined with sim-to-real transfer offers a general framework for developing locomotion controllers for legged robots. To facilitate successful deployment in the real world, smoothing techniques, such as low-pass filters and sm
Externí odkaz:
http://arxiv.org/abs/2410.11825
Autor:
Tuck, Victoria Marie, Chen, Pei-Wei, Fainekos, Georgios, Hoxha, Bardh, Okamoto, Hideki, Sastry, S. Shankar, Seshia, Sanjit A.
Multi-Robot Task Allocation (MRTA) is a problem that arises in many application domains including package delivery, warehouse robotics, and healthcare. In this work, we consider the problem of MRTA for a dynamic stream of tasks with task deadlines an
Externí odkaz:
http://arxiv.org/abs/2403.11737
Autor:
Rahmanian, Nima, Gupta, Michael, Soatto, Renzo, Nachuri, Srisai, Psenka, Michael, Ma, Yi, Sastry, S. Shankar
Robotic interaction in fast-paced environments presents a substantial challenge, particularly in tasks requiring the prediction of dynamic, non-stationary objects for timely and accurate responses. An example of such a task is ping-pong, where the ph
Externí odkaz:
http://arxiv.org/abs/2312.03024
Publikováno v:
CCTA (2021) 103-110
Decentralized planning for multi-agent systems, such as fleets of robots in a search-and-rescue operation, is often constrained by limitations on how agents can communicate with each other. One such limitation is the case when agents can communicate
Externí odkaz:
http://arxiv.org/abs/2203.02609
As predictive models are deployed into the real world, they must increasingly contend with strategic behavior. A growing body of work on strategic classification treats this problem as a Stackelberg game: the decision-maker "leads" in the game by dep
Externí odkaz:
http://arxiv.org/abs/2106.12529
Autor:
Maheshwari, Chinmay, Chiu, Chih-Yuan, Mazumdar, Eric, Sastry, S. Shankar, Ratliff, Lillian J.
Min-max optimization is emerging as a key framework for analyzing problems of robustness to strategically and adversarially generated data. We propose a random reshuffling-based gradient free Optimistic Gradient Descent-Ascent algorithm for solving c
Externí odkaz:
http://arxiv.org/abs/2106.09082
!TEX root = LCSS_main_max.tex The widespread adoption of nonlinear Receding Horizon Control (RHC) strategies by industry has led to more than 30 years of intense research efforts to provide stability guarantees for these methods. However, current the
Externí odkaz:
http://arxiv.org/abs/2103.15010
When an expert operates a perilous dynamic system, ideal constraint information is tacitly contained in their demonstrated trajectories and controls. The likelihood of these demonstrations can be computed, given the system dynamics and task objective
Externí odkaz:
http://arxiv.org/abs/2102.12554
In this work we present a multi-armed bandit framework for online expert selection in Markov decision processes and demonstrate its use in high-dimensional settings. Our method takes a set of candidate expert policies and switches between them to rap
Externí odkaz:
http://arxiv.org/abs/2010.15599
Autor:
Westenbroek, Tyler, Castaneda, Fernando, Agrawal, Ayush, Sastry, S. Shankar, Sreenath, Koushil
This paper introduces a framework for learning a minimum-norm stabilizing controller for a system with unknown dynamics using model-free policy optimization methods. The approach begins by first designing a Control Lyapunov Function (CLF) for a (poss
Externí odkaz:
http://arxiv.org/abs/2004.10331