Motion Macro Programming on Assistive Robotic Manipulators: Three Skill Types for Everyday Tasks

TL;DR

This paper introduces a modular macro programming approach for assistive robotic manipulators, enabling users to easily specify and execute everyday tasks through local, global, and hybrid skills without advanced technical knowledge.

Contribution

It proposes a novel framework of three skill types for intuitive macro programming, with detailed robot-agnostic implementation and validation through practical experiments.

Findings

Successful execution of tasks like scratching, sorting, and feeding.

User-friendly programming of skills without expert knowledge.

Open-source implementation available for broader use.

Abstract

Assistive robotic manipulators are becoming increasingly important for people with disabilities. Teleoperating the manipulator in mundane tasks is part of their daily lives. Instead of steering the robot through all actions, applying self-recorded motion macros could greatly facilitate repetitive tasks. Dynamic Movement Primitives (DMP) are a powerful method for skill learning via teleoperation. For this use case, however, they need simple heuristics to specify where to start, stop, and parameterize a skill without a background in computer science and academic sensor setups for autonomous perception. To achieve this goal, this paper provides the concept of local, global, and hybrid skills that form a modular basis for composing single-handed tasks of daily living. These skills are specified implicitly and can easily be programmed by users themselves, requiring only their basic robotic manipulator. The paper contributes all details for robot-agnostic implementations. Experiments validate the developed methods for exemplary tasks, such as scratching an itchy spot, sorting objects on a desk, and feeding a piggy bank with coins. The paper is accompanied by an open-source implementation at https://github.com/fzi-forschungszentrum-informatik/ArNe