SPACE: A Simulator for Physical Interactions and Causal Learning in 3D Environments
Nanyang Technological University（南洋理工大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局（A*STAR））
Jiafei Duan, Samson Yu Bai Jian, Cheston Tan
备注：
Accepted to ICCV 21, Simulation Technology for Embodied AI (SEAI) Workshop
Recent advancements in deep learning, computer vision, and embodied AI have given rise to synthetic causal reasoning video datasets. These datasets facilitate the development of AI algorithms that can reason about physical interactions between objects. However, datasets thus far have primarily focused on elementary physical events such as rolling or falling. There is currently a scarcity of datasets that focus on the physical interactions that humans perform daily with objects in the real world. To address this scarcity, we introduce SPACE: A Simulator for Physical Interactions and Causal Learning in 3D Environments. The SPACE simulator allows us to generate the SPACE dataset, a synthetic video dataset in a 3D environment, to systematically evaluate physics-based models on a range of physical causal reasoning tasks. Inspired by daily object interactions, the SPACE dataset comprises videos depicting three types of physical events: containment, stability and contact. These events make up the vast majority of the basic physical interactions between objects. We then further evaluate it with a state-of-the-art physics-based deep model and show that the SPACE dataset improves the learning of intuitive physics with an approach inspired by curriculum learning. Repository: https://github.com/jiafei1224/SPACE
Simulator-Grounded Large Language Models for Industrial Causal Reasoning: Tool-Use, Structured Injection, and Plant-Portable Retrieval for Wastewater Treatment Decision Support
Max Planck Institute for Informatics（马克斯·普朗克信息研究所）；Singapore University of Technology and Design（新加坡科技设计大学）；A*STAR（新加坡科技研究局）；Google（谷歌）；Saarbrücken Research Center for Visual Computing, Interaction and Artificial Intelligence（斯图加特视觉计算、交互与人工智能研究中心）
ChronoAgentic: A Code-based Multi-Agent World Simulator for Physically Grounded Simulation Construction
Department of Mechanical & Aerospace Engineering, University of Wisconsin-Madison（威斯康星大学麦迪逊分校机械与航空航天工程系）；School of Computer, Data, and Information Sciences, University of Wisconsin-Madison（威斯康星大学麦迪逊分校计算机、数据与信息科学学院）
Language Agents Meet Causality -- Bridging LLMs and Causal World Models
University of Amsterdam（阿姆斯特丹大学）；Institute for Logic, Language and Computation (ILLC)（逻辑、语言与计算研究所）；University of Edinburgh（爱丁堡大学）；Institute for Language, Cognition and Computation (ILCC)（语言、认知与计算研究所）
Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models
Department of Mechanical & Industrial Engineering（机械与工业工程系）；Northeastern University（东北大学）；Department of Industrial Engineering and Management Sciences（工业工程与管理科学系）；Department of Mechanical Engineering（机械工程系）；Northwestern University（西北大学）
