Zobrazeno 1 - 10
of 396
pro vyhledávání: '"Liu Weiyu"'
Autor:
Liu Weiyu
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
Aiming at the current lack of innovation in Chinese ceramic culture, this paper proposes an inheritance and innovation path combining ceramic digitization technology and ceramic virtual display technology. Based on the three-dimensional morphology re
Externí odkaz:
https://doaj.org/article/09d64c88a1b74b7eba55a3006adb07b2
Autor:
Sun, Fan-Yun, Liu, Weiyu, Gu, Siyi, Lim, Dylan, Bhat, Goutam, Tombari, Federico, Li, Manling, Haber, Nick, Wu, Jiajun
Open-universe 3D layout generation arranges unlabeled 3D assets conditioned on language instruction. Large language models (LLMs) struggle with generating physically plausible 3D scenes and adherence to input instructions, particularly in cluttered s
Externí odkaz:
http://arxiv.org/abs/2412.02193
Autor:
Liu, Yunong, Eyzaguirre, Cristobal, Li, Manling, Khanna, Shubh, Niebles, Juan Carlos, Ravi, Vineeth, Mishra, Saumitra, Liu, Weiyu, Wu, Jiajun
Shape assembly is a ubiquitous task in daily life, integral for constructing complex 3D structures like IKEA furniture. While significant progress has been made in developing autonomous agents for shape assembly, existing datasets have not yet tackle
Externí odkaz:
http://arxiv.org/abs/2411.11409
Autor:
Li, Manling, Zhao, Shiyu, Wang, Qineng, Wang, Kangrui, Zhou, Yu, Srivastava, Sanjana, Gokmen, Cem, Lee, Tony, Li, Li Erran, Zhang, Ruohan, Liu, Weiyu, Liang, Percy, Fei-Fei, Li, Mao, Jiayuan, Wu, Jiajun
We aim to evaluate Large Language Models (LLMs) for embodied decision making. While a significant body of work has been leveraging LLMs for decision making in embodied environments, we still lack a systematic understanding of their performance becaus
Externí odkaz:
http://arxiv.org/abs/2410.07166
Autor:
Jin, Emily, Huang, Zhuoyi, Fränken, Jan-Philipp, Liu, Weiyu, Cha, Hannah, Brockbank, Erik, Wu, Sarah, Zhang, Ruohan, Wu, Jiajun, Gerstenberg, Tobias
Reconstructing past events requires reasoning across long time horizons. To figure out what happened, we need to use our prior knowledge about the world and human behavior and draw inferences from various sources of evidence including visual, languag
Externí odkaz:
http://arxiv.org/abs/2410.01926
Ambiguities are common in human-robot interaction, especially when a robot follows user instructions in a large collocated space. For instance, when the user asks the robot to find an object in a home environment, the object might be in several place
Externí odkaz:
http://arxiv.org/abs/2409.17004
In this paper, we propose composable part-based manipulation (CPM), a novel approach that leverages object-part decomposition and part-part correspondences to improve learning and generalization of robotic manipulation skills. By considering the func
Externí odkaz:
http://arxiv.org/abs/2405.05876
This paper presents a framework for learning state and action abstractions in sequential decision-making domains. Our framework, planning abstraction from language (PARL), utilizes language-annotated demonstrations to automatically discover a symboli
Externí odkaz:
http://arxiv.org/abs/2405.03864
3D visual grounding is a challenging task that often requires direct and dense supervision, notably the semantic label for each object in the scene. In this paper, we instead study the naturally supervised setting that learns from only 3D scene and Q
Externí odkaz:
http://arxiv.org/abs/2404.19696
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