MAkEable

Abstract: Achieving versatile mobile manipulation in human-centered environments relies on efficiently transferring learned tasks and experiences across robots and environments. Our framework, MAkEable, facilitates seamless transfer of capabilities and knowledge across tasks, environments, and robots by integrating an affordance-based task description into the memory-centric cognitive architecture of the ARMAR humanoid robot family. This integration enables the sharing of experiences for transfer learning. By representing actions through affordances, MAkEable provides a unified framework for autonomous manipulation of known and unknown objects in diverse environments. Real-world experiments demonstrate MAkEable’s effectiveness across multiple robots, tasks, and environments, including grasping objects, bimanual manipulation, and skill transfer between humanoid robots.

For more information please visit our project page.