Motion Macro Programming on Assistive Robotic Manipulators: Three Skill Types for Everyday Tasks
This work addresses the challenge of making assistive robotics more accessible for people with disabilities by reducing the complexity of programming daily tasks, though it is incremental as it builds on existing Dynamic Movement Primitives with new heuristics.
The paper tackled the problem of simplifying motion macro programming for assistive robotic manipulators used by people with disabilities, by introducing local, global, and hybrid skill types that enable users to program repetitive tasks without specialized knowledge, and validated these methods in experiments with tasks like scratching and sorting objects.
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