Trabajo presentado en la IEEE International Conference on Robotics and Automation (ICRA), celebrado en Brisbane (Australia), del 21 al 25 de mayo de 2018 ; For a safe and successful daily living assistance, far from the highly controlled environment of a factory, robots should be able to adapt to ever-changing situations. Programming such a robot is a tedious process that requires expert knowledge. An alternative is to rely on a high-level planner, but the generic symbolic representations used are not well suited to particular robot executions. Contrarily, motion primitives encode robot motions in a way that can be easily adapted to different situations. This paper presents a combined framework that exploits the advantages of both approaches. The number of required symbolic states is reduced, as motion primitives provide “smart actions” that take the current state and cope online with variations. Symbolic actions can include interactions (e.g., ask and inform) that are difficult to demonstrate. We show that the proposed framework can adapt to the user preferences (in terms of robot speed and robot verbosity), can readjust the trajectories based on the user movements, and can handle unforeseen situations. Experiments are performed in a shoe-dressing scenario. This scenario is particularly interesting because it involves a sufficient number of actions, and the human-robot interaction requires the handling of user preferences and unexpected reactions. ; This work has been supported by the ERA-Net CHIST-ERA project IDRESS (Spain’s Ministry of Economy and Competitiveness PCIN-2015- 147, Swiss National Science Foundation 20CH21-16085). It is also supported by the Spanish State Research Agency through the Mara de Maeztu Seal of Excellence to IRI (MDM-2016-0656). Gerard Canal is also supported by the Spanish Ministry of Education, Culture and Sport via a FPU doctoral grant FPU15/00504. ; Peer reviewed
Joining high-level symbolic planning with low-level motion primitives in adaptive HRI: application to dressing assistance
2018-01-01
Conference paper
Electronic Resource
English
DDC: | 629 |
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