A practical DMPs Implementation for Skill Creation and Teleoperation with Assistive Manipulators
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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 skills could greatly facilitate repetitive tasks. Dynamic Movement Primitives (DMP) are a powerful method for skill learning. For this use case, however, they need a simple heuristic to specify where to start and stop a skill without additional sensors. This paper provides the concept of local, global, and hybrid skills that form a modular basis for composing single-handed tasks with ease. A focus is on presenting the necessary mathematical details to support custom implementations with assistive robot arms. Experiments validate the developed methods for 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
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