MAkEable：面向可迁移移动操作技能的以记忆为中心且基于可供性的任务执行框架
MAkEable: Memory-centered and Affordance-based Task Execution Framework for Transferable Mobile Manipulation Skills
AI总结：
本文提出MAkEable框架，通过将基于可供性的任务描述集成到ARMAR类人机器人以记忆为中心的认知架构中，实现了跨任务、环境和机器人的单臂及双臂移动操作技能迁移学习。
摘要：
为了在以人为中心的环境中执行多样化的移动操作任务，将学习到的任务和经验从一台机器人高效迁移到另一台机器人或跨不同环境迁移的能力是关键。在本文中，我们提出了MAkEable，这是一个通用的单臂和双臂移动操作框架，可促进跨不同任务、环境和机器人的能力与知识迁移。我们的框架将基于可供性的任务描述集成到ARMAR类人机器人家族以记忆为中心的认知架构中，该架构支持共享经验和示范以进行迁移学习。通过可供性（即机器人与其环境的交互可能性）来表示移动操作动作，我们为在各种环境中自主单臂和双臂操作已知和未知物体提供了一个统一框架。我们在真实世界实验中展示了该框架对多机器人、任务和环境的适用性。这包括抓取已知和未知物体、物体放置、双臂物体抓取、在抽屉打开场景中跨两个不同类人机器人的记忆驱动技能迁移，以及从人类示范中学习的倾倒任务。
英文摘要：
To perform versatile mobile manipulation tasks in human-centered environments, the ability to efficiently transfer learned tasks and experiences from one robot to another or across different environments is key. In this paper, we present MAkEable, a versatile uni- and multi-manual mobile manipulation framework that facilitates the transfer of capabilities and knowledge across different tasks, environments, and robots. Our framework integrates an affordance-based task description into the memory-centric cognitive architecture of the ARMAR humanoid robot family, which supports the sharing of experiences and demonstrations for transfer learning. By representing mobile manipulation actions through affordances, i.e., interaction possibilities of the robot with its environment, we provide a unifying framework for the autonomous uni- and multi-manual manipulation of known and unknown objects in various environments. We demonstrate the applicability of the framework in real-world experiments for multiple robots, tasks, and environments. This includes grasping known and unknown objects, object placing, bimanual object grasping, memory-enabled skill transfer in a drawer opening scenario across two different humanoid robots, and a pouring task learned from human demonstration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
