Extracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding
Massachusetts Institute of Technology（麻省理工学院）
William Chen, Siyi Hu, Rajat Talak, Luca Carlone
备注：
4 pages (excluding references and appendix), 2 figures, 2 tables. Submitted to Robotics: Science and Systems 2022 2nd Workshop on Scaling Robot Learning. Corrected typos and notation
Semantic 3D scene understanding is a problem of critical importance in robotics. While significant advances have been made in simultaneous localization and mapping algorithms, robots are still far from having the common sense knowledge about household objects and their locations of an average human. We introduce a novel method for leveraging common sense embedded within large language models for labelling rooms given the objects contained within. This algorithm has the added benefits of (i) requiring no task-specific pre-training (operating entirely in the zero-shot regime) and (ii) generalizing to arbitrary room and object labels, including previously-unseen ones -- both of which are highly desirable traits in robotic scene understanding algorithms. The proposed algorithm operates on 3D scene graphs produced by modern spatial perception systems, and we hope it will pave the way to more generalizable and scalable high-level 3D scene understanding for robotics.
Large Language Model for Extracting Complex Contract Information in Industrial Scenes
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, China（上海智能自主系统研究院，同济大学）；School of Computer Science and Technology, East China Normal University, Shanghai, China（计算机科学与技术学院，华东师范大学）
Learning Robot Visual Navigation in Crowds via Intention-Aware Scene Representations
Shenzhen Key Laboratory of Robotics Perception and Intelligence, Department of Electronic and Electrical Engineering, SUSTech（南方科技大学电子与电气工程系深圳市机器人感知与智能重点实验室）；Jiaxing Research Institute, SUSTech（南方科技大学嘉兴研究院）；Research Institute of MA&EI, Peng Cheng Laboratory（鹏城实验室机器人与人工智能研究所）
PAS-SLAM: A Visual SLAM System for Planar Ambiguous Scenes
School of Electronic Information and Electrical Engineering, Dalian University of Technology（大连理工大学电子信息与电气工程学部）；Department of Automation, Tsinghua University（清华大学自动化系）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）
Where to Fetch: Extracting Visual Scene Representation from Large Pre-Trained Models for Robotic Goal Navigation
School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；Campus Security Smart Brain Shandong Engineering Research Center（山东校园安全智能脑工程研究中心）
