Predicting Stable Configurations for Semantic Placement of Novel Objects
NVIDIA Corporation（英伟达公司）；Paul G. Allen School of Computer Science & Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；School of Computing, University of Utah（犹他大学计算学院）
Chris Paxton, Chris Xie, Tucker Hermans, Dieter Fox
Human environments contain numerous objects configured in a variety of arrangements. Our goal is to enable robots to repose previously unseen objects according to learned semantic relationships in novel environments. We break this problem down into two parts: (1) finding physically valid locations for the objects and (2) determining if those poses satisfy learned, high-level semantic relationships. We build our models and training from the ground up to be tightly integrated with our proposed planning algorithm for semantic placement of unknown objects. We train our models purely in simulation, with no fine-tuning needed for use in the real world. Our approach enables motion planning for semantic rearrangement of unknown objects in scenes with varying geometry from only RGB-D sensing. Our experiments through a set of simulated ablations demonstrate that using a relational classifier alone is not sufficient for reliable planning. We further demonstrate the ability of our planner to generate and execute diverse manipulation plans through a set of real-world experiments with a variety of objects.
AnyPlace: Learning Generalized Object Placement for Robot Manipulation
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Shanghai Jiao Tong University（上海交通大学）；Wilfrid Laurier University（威尔弗里德·劳里埃大学）；Acceleration Consortium（加速联盟）；Georgia Institute of Technology（佐治亚理工学院）
Lan-grasp: Using Large Language Models for Semantic Object Grasping and Placement
Department of Computer Science and Artificial Intelligence, University of Technology Nuremberg（计算机科学与人工智能系，图恩技术大学）；Department of Engineering ``Enzo Ferrari'' (DIEF), University of Modena and Reggio Emilia（恩佐·费尔拉蒂工程系（DIEF），摩德纳和雷吉奥艾米利亚大学）；Technical University of Munich（慕尼黑技术大学）
Predicting Dense and Context-aware Cost Maps for Semantic Robot Navigation
University of Bonn（波恩大学）；Robert Bosch GmbH（罗伯特·博世有限公司）；University of Oxford（牛津大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）
Learning to Reorient Objects with Stable Placements Afforded by Extrinsic Supports
The Chinese University of Hong Kong（香港中文大学）；Southern University of Science and Technology（南方科技大学）；Shenzhen Research Institute of the Chinese University of Hong Kong（香港中文大学深圳研究院）
