costs are updated to favor the observations of the execution. We evaluate the methods in a shoe-fitting task, both with simulated and real experiments.
- Chapter 5demonstrates how the use of theplanningtechniques can be joined with “smart” low-level controllers for more efficient and robust task design. We show its applicability to a shoe-fitting scenario and demonstrate how some robustness arises when this approach is used, which can solve untaught situations. We also argue how this union of concepts al- lows for easier demonstrations and domain description, lowering the system’s complexity. This is linked with all the steps of theFUTEframework.
- Chapter 6 introduces a novel algorithm for providing suggestions in planning. To do
so, an extension to the ROSPlan framework has been proposed to ease the use of more expressive language like RDDL. This allows us to express a richer reward function that involves the preferences and the actions to provide the suggestions. Such suggestions are predicates that improve the total task performance when available. This task performance is measured in terms of the total reward of the plan. We show how this method can be used to provide preference suggestions even when some preferences are already grounded, and even provide change suggestions to the user. The system is evaluated in simulated experiments in threePhysically Assistive Roboticstasks.
- Chapter 7analyzes the use of the preferences proposed inChapter 3and the other adap- tation methods through task planning in a user study. We evaluate the user’s ability to determine in which executions their own chosen preferences are used, and whether they can distinguish the changes in the robot’s behavior produced by such preferences. The study gives insights on the impact of said preferences in the assistive robotics tasks, and show promising results on the use of behavior adaptation for effective assistance.
- Chapter 8provides the conclusions to this thesis with some of the future work and direc- tions of the research proposed in the thesis.
Appendices
- Appendix Apresents the list of academic publications resulting of this thesis. - Appendix Banalyzes some safety strategies forphysical Human-Robot Interaction.
- Appendix Cshows some experiments to support the use ofprobabilistic planningin robotics using theROSPlanextensions described inChapter 6.
- Appendix D transcribes the questionnaire used to evaluate the user study performed in
2
Personal assistive robots for non-technical users
This chapter presents a robot behavior personalization framework focused on assistive tasks. The framework defines a three-step methodology to guide the development of adaptive and personalized assistive tasks, and more specifically the physical ones. A demonstration of the framework’s use is provided in the context of feeding assistance, showing how physical adapta- tions can be performed by untrained users.
This work has been published in [6].
2.1 Introduction
Robot adaptation is especially useful in cases of users in need of assistance. Such users, with their own limitations, are usually unable of controlling or adapting a robot to suit their needs. And this vulnerability may hinder the use of such assistive devices, making them unusable. However, it is not clear how the process of personalization should be performed. We envisage the robot acquisition process as the robot being built, programmed, and shipped to a hypothetical user’s home. However, personalizing the robot at building time or programming time is hard and costly, and the users may not still know their actual needs. But doing it at home may not be viable for dependent and possibly non-technical users.
In this chapter, we propose a novel Robot Personalization framework namedFUTE(detailed inSection 2.3), that takes into account the user and allows concrete adaptation of generic pre- trained skills. In our framework, the robot is pre-trained at the factory with a set of abilities. Afterward, when it arrives at the user’s home, a non-expert teacher (the user itself or a caregiver) must have the freedom to adapt such skills to his/her preferences, or even teach the robot new ones.
Second, we explore how to perform this training by using Learning-by-Demonstration tech- niques combined with acompliant robot controller[20]. We propose two interaction strategies:
16 Personal assistive robots for non-technical users
the teacher intervening in the robot motion, and the demonstration of a completely new trajec- tory.
In the third place, we test the applicability of the proposedFUTEframework in an assistive task consisting of feeding a person. As feeding can be very complex, we focus on a specific aspect: how the robot approaches the cutlery to feed the person (seeFigure 2.1). We will show how our system can extract the relevant aspects of the feeding task. Observe that, depending on the mobility and preferences of the user, the robot must wait with the food at some distance or introduce the food inside the mouth. Moreover, the feeding motion has to be adapted to the kind of food, for example, yogurt or fries as seen inFigure 2.1b.
(a) Caregiver personalizing a spoon feeding skill. (b) A user eating from a fork. Figure 2.1: Assistive personalized feeding application example.