Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Firoozi, Roya"'
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
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:
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
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
For safe navigation in dynamic uncertain environments, robotic systems rely on the perception and prediction of other agents. Particularly, in occluded areas where cameras and LiDAR give no data, the robot must be able to reason about potential movem
Externí odkaz:
http://arxiv.org/abs/2211.09156
Game-theoretic motion planners are a potent solution for controlling systems of multiple highly interactive robots. Most existing game-theoretic planners unrealistically assume a priori objective function knowledge is available to all agents. To addr
Externí odkaz:
http://arxiv.org/abs/2209.12968
Publikováno v:
IEEE Transactions on Energy Conversion (2021)
High power operation in extreme fast charging significantly increases the risk of internal faults in Electric Vehicle batteries which can lead to accelerated battery failure. Early detection of these faults is crucial for battery safety and widesprea
Externí odkaz:
http://arxiv.org/abs/2105.02169
Publikováno v:
IEEE Transactions on Vehicular Technology, 2024
This work presents a distributed method for multi-vehicle coordination based on nonlinear model predictive control (NMPC) and dual decomposition. Our approach allows the vehicles to coordinate in tight spaces (e.g., busy highway lanes or parking lots
Externí odkaz:
http://arxiv.org/abs/2006.11492
Autor:
Smith, Stanley W., Kim, Yeojun, Guanetti, Jacopo, Li, Ruolin, Firoozi, Roya, Wootton, Bruce, Kurzhanskiy, Alexander A., Borrelli, Francesco, Horowitz, Roberto, Arcak, Murat
In this paper we present a model-predictive control (MPC) based approach for vehicle platooning in an urban traffic setting. Our primary goal is to demonstrate that vehicle platooning has the potential to significantly increase throughput at intersec
Externí odkaz:
http://arxiv.org/abs/2006.10272
Advances in vehicular communication technologies are expected to facilitate cooperative driving. Connected and Automated Vehicles (CAVs) are able to collaboratively plan and execute driving maneuvers by sharing their perceptual knowledge and future p
Externí odkaz:
http://arxiv.org/abs/2003.08595