Selecting an optimal robot, its base pose, and trajectory for a given task is currently mainly done by human expertise or trial and error. To evaluate automatic approaches to this combined optimization problem, we introduce a benchmark suite encompassing a unified format for robots, environments, and task descriptions. Our benchmark suite is especially useful for modular robots, where the multitude of robots that can be assembled creates a host of additional parameters to optimize. We include tasks such as machine tending and welding in synthetic environments and 3D scans of real-world machine shops. All benchmarks are accessible through https://cobra.cps.cit.tum.de, a platform to conveniently share, reference, and compare tasks, robot models, and solutions.
RH20T-P: A Primitive-Level Robotic Dataset Towards Composable Generalization Agents
Shanghai AI Laboratory（上海人工智能实验室）；School of Software, Beihang University（北京航空航天大学软件学院）；Shanghai Jiao Tong University（上海交通大学）；University of Sydney（悉尼大学）
COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty
Oxford Robotics Institute, Dept. Engineering Science, University of Oxford, UK（牛津大学机器人研究所）；Microsoft Research, Redmond, WA, USA（微软研究院）；Bristol Robotics Laboratory, School of Engineering, University of the West of England, Bristol, UK（布里斯托尔机器人实验室）
Generalizable Skill Learning for Construction Robots with Crowdsourced Natural Language Instructions, Composable Skills Standardization, and Large Language Model
Virginia Tech（弗吉尼亚理工大学）；University of Michigan（密歇根大学）
Universität Klagenfurt（克拉根福大学）；Alias Robotics；Institute for Robotics and Mechatronics, Joanneum Research（机器人与机电一体化研究所，格拉茨研究中心）；Department of Informatics of University of Minho（米尼奥大学信息学系）；High-Assurance Software Laboratory of University of Minho（米尼奥大学高可信软件实验室）；INESC TEC；Robotics Group at University of León（莱昂大学机器人学小组）；Istituto di Informatica e Telematica, Consiglio Nazionale delle Ricerche (CNR)（意大利国家研究委员会信息与电信研究所）
