Zobrazeno 1 - 10
of 2 688
pro vyhledávání: '"Firoozi A"'
Autor:
Kim, Chan, Kim, Keonwoo, Oh, Mintaek, Baek, Hanbi, Lee, Jiyang, Jung, Donghwi, Woo, Soojin, Woo, Younkyung, Tucker, John, Firoozi, Roya, Seo, Seung-Woo, Schwager, Mac, Kim, Seong-Woo
Large language models (LLMs) have shown significant potential in guiding embodied agents to execute language instructions across a range of tasks, including robotic manipulation and navigation. However, existing methods are primarily designed for sta
Externí odkaz:
http://arxiv.org/abs/2409.10027
Let $a,b$ be fixed positive coprime integers. For a positive integer $g$, write $N_k(g)$ for the set of lattice paths from the startpoint $(0,0)$ to the endpoint $(ga,gb)$ with steps restricted to $\{(1,0), (0,1)\}$, having exactly $k$ flaws (lattice
Externí odkaz:
http://arxiv.org/abs/2406.09590
Autor:
Shorinwa, Ola, Tucker, Johnathan, Smith, Aliyah, Swann, Aiden, Chen, Timothy, Firoozi, Roya, Kennedy III, Monroe, Schwager, Mac
We present Splat-MOVER, a modular robotics stack for open-vocabulary robotic manipulation, which leverages the editability of Gaussian Splatting (GSplat) scene representations to enable multi-stage manipulation tasks. Splat-MOVER consists of: (i) ASK
Externí odkaz:
http://arxiv.org/abs/2405.04378
Autor:
Ren, Xin Yue, Firoozi, Dena
In this paper, we address linear-quadratic-Gaussian (LQG) risk-sensitive mean field games (MFGs) with common noise. In this framework agents are exposed to a common noise and aim to minimize an exponential cost functional that reflects their risk sen
Externí odkaz:
http://arxiv.org/abs/2403.03915
Autor:
Liu, Hanchao, Firoozi, Dena
This paper presents a comprehensive study of linear-quadratic (LQ) mean field games (MFGs) in Hilbert spaces, generalizing the classic LQ MFG theory to scenarios involving $N$ agents with dynamics governed by infinite-dimensional stochastic equations
Externí odkaz:
http://arxiv.org/abs/2403.01012
Autor:
Firoozi, Roya, Tucker, Johnathan, Tian, Stephen, Majumdar, Anirudha, Sun, Jiankai, Liu, Weiyu, Zhu, Yuke, Song, Shuran, Kapoor, Ashish, Hausman, Karol, Ichter, Brian, Driess, Danny, Wu, Jiajun, Lu, Cewu, Schwager, Mac
We survey applications of pretrained foundation models in robotics. Traditional deep learning models in robotics are trained on small datasets tailored for specific tasks, which limits their adaptability across diverse applications. In contrast, foun
Externí odkaz:
http://arxiv.org/abs/2312.07843
Publikováno v:
Journal of Manufacturing Technology Management, 2024, Vol. 35, Issue 4, pp. 894-917.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JMTM-07-2022-0256
Game-theoretic motion planners are a powerful tool for the control of interactive multi-agent robot systems. Indeed, contrary to predict-then-plan paradigms, game-theoretic planners do not ignore the interactive nature of the problem, and simultaneou
Externí odkaz:
http://arxiv.org/abs/2310.12958
Video violence recognition based on deep learning concerns accurate yet scalable human violence recognition. Currently, most state-of-the-art video violence recognition studies use CNN-based models to represent and categorize videos. However, recent
Externí odkaz:
http://arxiv.org/abs/2310.03108
This paper presents a dynamic game framework to analyze the role of large banks in the interbank market. By extending existing models, we incorporate a major bank as a dynamic decision-maker interacting with multiple small banks. Using the mean field
Externí odkaz:
http://arxiv.org/abs/2305.17830