Zobrazeno 1 - 10
of 3 511
pro vyhledávání: '"Krishna, Murthy"'
Recent advancements in learned 3D representations have enabled significant progress in solving complex robotic manipulation tasks, particularly for rigid-body objects. However, manipulating granular materials such as beans, nuts, and rice, remains ch
Externí odkaz:
http://arxiv.org/abs/2410.09740
Autor:
Rivera, Corban, Byrd, Grayson, Paul, William, Feldman, Tyler, Booker, Meghan, Holmes, Emma, Handelman, David, Kemp, Bethany, Badger, Andrew, Schmidt, Aurora, Jatavallabhula, Krishna Murthy, de Melo, Celso M, Seenivasan, Lalithkumar, Unberath, Mathias, Chellappa, Rama
Robotic planning and execution in open-world environments is a complex problem due to the vast state spaces and high variability of task embodiment. Recent advances in perception algorithms, combined with Large Language Models (LLMs) for planning, of
Externí odkaz:
http://arxiv.org/abs/2410.06108
Autor:
Verbas, Omer, Cokyasar, Taner, de Camargo, Pedro Veiga, Gurumurthy, Krishna Murthy, Zuniga-Garcia, Natalia, Auld, Joshua
This study presents a transit routing, assignment, and simulation framework which is fully embedded in a multimodal, multi-agent transportation demand and supply modeling platform. POLARIS, a high-performance agent-based simulation platform, efficien
Externí odkaz:
http://arxiv.org/abs/2408.05176
We tackle the problem of learning an implicit scene representation for 3D instance segmentation from a sequence of posed RGB images. Towards this, we introduce 3DIML, a novel framework that efficiently learns a neural label field which can render 3D
Externí odkaz:
http://arxiv.org/abs/2403.19797
Autor:
Auld, Joshua, Cook, Jamie, Gurumurthy, Krishna Murthy, Khan, Nazmul, Mansour, Charbel, Rousseau, Aymeric, Sahin, Olcay, de Souza, Felipe, Verbas, Omer, Zuniga-Garcia, Natalia
Rapid technological progress and innovation in the areas of vehicle connectivity, automation and electrification, new modes of shared and alternative mobility, and advanced transportation system demand and supply management strategies, have motivated
Externí odkaz:
http://arxiv.org/abs/2403.14669
Autor:
Keetha, Nikhil, Karhade, Jay, Jatavallabhula, Krishna Murthy, Yang, Gengshan, Scherer, Sebastian, Ramanan, Deva, Luiten, Jonathon
Dense simultaneous localization and mapping (SLAM) is crucial for robotics and augmented reality applications. However, current methods are often hampered by the non-volumetric or implicit way they represent a scene. This work introduces SplaTAM, an
Externí odkaz:
http://arxiv.org/abs/2312.02126
Autor:
Spielberg, Andrew, Zhong, Fangcheng, Rematas, Konstantinos, Jatavallabhula, Krishna Murthy, Oztireli, Cengiz, Li, Tzu-Mao, Nowrouzezahrai, Derek
Originally designed for applications in computer graphics, visual computing (VC) methods synthesize information about physical and virtual worlds, using prescribed algorithms optimized for spatial computing. VC is used to analyze geometry, physically
Externí odkaz:
http://arxiv.org/abs/2312.04574
Autor:
Vakayil, Akhil, de Souza, Felipe, Cokyasar, Taner, Gurumurthy, Krishna Murthy, Larson, Jeffrey
Transportation network companies (TNCs) have become a highly utilized transportation mode over the past years. At their emergence, TNCs were serving ride requests one by one. However, the economic and environmental benefits of ridesharing encourages
Externí odkaz:
http://arxiv.org/abs/2311.06160
Autor:
Omama, Mohammad, Inani, Pranav, Paul, Pranjal, Yellapragada, Sarat Chandra, Jatavallabhula, Krishna Murthy, Chinchali, Sandeep, Krishna, Madhava
We present an autonomous navigation system that operates without assuming HD LiDAR maps of the environment. Our system, ALT-Pilot, relies only on publicly available road network information and a sparse (and noisy) set of crowdsourced language landma
Externí odkaz:
http://arxiv.org/abs/2310.02324
Autor:
Choudhary, Tushar, Dewangan, Vikrant, Chandhok, Shivam, Priyadarshan, Shubham, Jain, Anushka, Singh, Arun K., Srivastava, Siddharth, Jatavallabhula, Krishna Murthy, Krishna, K. Madhava
Talk2BEV is a large vision-language model (LVLM) interface for bird's-eye view (BEV) maps in autonomous driving contexts. While existing perception systems for autonomous driving scenarios have largely focused on a pre-defined (closed) set of object
Externí odkaz:
http://arxiv.org/abs/2310.02251