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.

翻译：为实现以人为中心环境中的多功能移动操作任务，跨机器人或跨环境高效迁移已学任务与经验的能力至关重要。本文提出MAkEable——一种支持跨任务、跨环境、跨机器人能力与知识迁移的通用单/多臂移动操作框架。该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，支持通过共享经验与示范实现迁移学习。通过将移动操作行为表示为可供性（即机器人与环境交互的可能性），我们为不同环境中已知与未知物体的自主单/多臂操作提供了统一框架。通过多机器人、多任务、多环境的真实世界实验验证了框架的适用性，包括：已知与未知物体抓取、物体放置、双臂物体抓取、跨双类人机器人的抽屉开启场景中的记忆驱动技能迁移，以及通过人类示范学习的倒水任务。