Gerard Canal, Carme Torras, and Guillem Alenyà
Abstract: Task planning in human-robot environments tends to be particularly complex as it involves additional uncertainty introduced by the human user. Several plans, entailing few or various differences, can be obtained to solve the same given task. To choose among them, the usual least-cost plan criteria is not necessarily the best option, because here, human constraints and preferences come into play. Knowing these user preferences is very valuable to select an appropriate plan, but the preference values are usually hard to obtain. In this context, we propose the Space-of-Plans-based Suggestions (SoPS) algorithm that can provide suggestions for some planning predicates, which are used to define the state of the environment in a task planning problem where actions modify the predicates. We denote these predicates as suggestible predicates, for which user preferences are a particular case. The algorithm is able to analyze the potential effect of the unknown predicates and provide suggestions to values for these unknown predicates that may produce better plans. Additionally, it is able to suggest changes to already known values that potentially improve the obtained reward. The approach utilizes a Space of Plans Tree structure to represent a subset of the space of plans. The tree is traversed to find the predicates and the value that would most increase the reward, and output them as a suggestion to the user. Our exhaustive evaluation using three preference-based assistive robotics domains shows how the proposed algorithms can improve task performance by suggesting the most effective predicate values first.
Here we show examples of users with different preferences performing the feeding task with a robot.
Here we show examples of users with different preferences performing the shoe fitting task with a robot.
Here we show examples of users with different preferences performing the jacket dressing task with the robots.