In order to offer simple and convenient assistance for the elderly and disabled to take care of themselves, we propose a general learning and generalization approach for a service robot to accomplish specified tasks autonomously in an unstructured home environment. This approach firstly learns the required tasks by learning from demonstration (LfD) and represents the learned tasks with dynamic motion primitives (DMPs), so as to easily generalize them to a new environment only with little modification. Furthermore, we integrate dynamic potential field (DPF) with the above DMPs model to realize the autonomous obstacle avoidance function of a service robot. This approach is validated on the wheelchair mounted robotic arm (WMRA) by performing serial experiments of placing a cup on the table with an obstacle or without obstacle on its motion path.
With the purpose to offer simple and convenient assistance for the elders with disabilities to take care of themselves in activities of daily living, we present a motion primitives learning method based on robot learning from demonstration to improve the intelligence and adaptability of the wheelchair-mounted robotic arm. This method adopts the beta process autoregressive hidden Markov model to segment the demonstrations of related task, acquire the contained motion primitives, and recognize the repeated motion primitives. After that, it adopts the dynamic movement primitives to adjust the related motion primitives according to the task instructions by the wheelchair-mounted robotic arm users, so as to replay the demonstrated task in a new environment. This learning framework is validated on a 6-degree-of-freedom JACO robotic arm, performing the tasks of drinking water from the bottle through a straw and pouring water from the bottle to the cup.
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