Fabian Reister | Searcharxiv

arXiv2024

MAkEable: Memory-centered and Affordance-based Task Execution Framework for Transferable Mobile Manipulation Skills

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.

cs.RO↗

arXiv2023

A Memory System of a Robot Cognitive Architecture and its Implementation in ArmarX

Cognitive agents such as humans and robots perceive their environment through an abundance of sensors producing streams of data that need to be processed to generate intelligent behavior. A key question of cognition-enabled and AI-driven robotics is how to organize and manage knowledge efficiently in a cognitive robot control architecture. We argue, that memory is a central active component of such architectures that mediates between semantic and sensorimotor representations, orchestrates the flow of data streams and events between different processes and provides the components of a cognitive architecture with data-driven services for the abstraction of semantics from sensorimotor data, the parametrization of symbolic plans for execution and prediction of action effects. Based on related work, and the experience gained in developing our ARMAR humanoid robot systems, we identified conceptual and technical requirements of a memory system as central component of cognitive robot control architecture that facilitate the realization of high-level cognitive abilities such as explaining, reasoning, prospection, simulation and augmentation. Conceptually, a memory should be active, support multi-modal data representations, associate knowledge, be introspective, and have an inherently episodic structure. Technically, the memory should support a distributed design, be access-efficient and capable of long-term data storage. We introduce the memory system for our cognitive robot control architecture and its implementation in the robot software framework ArmarX. We evaluate the efficiency of the memory system with respect to transfer speeds, compression, reproduction and prediction capabilities.

cs.AI↗