Proactivity in Robots

My research is on proactiveness in robots. By the term proactive I understand a robot that is able to generate and select among its own goals and pursue activities towards their achievement. Consider the following example: Anna, who owns a personal robot, has instructions from her physician to take pills daily at meal times. Not taking the pills can result in Anna being unwell. Throughout the day, there are moments in which it would be beneficial for the robot to proactively prompt Anna to take her pills, and moments in which it would not be. For instance, it would be pedantic to remind her of the pills at breakfast, but it may be adequate at lunch and really needed at dinner. If Anna goes to bed and has not taken her pills yet, the robot may become more invasive and bring the pills to her. The robot should also bring the pills at breakfast, if it knows that Anna will be out for the rest of the day. The decision on when to act and what to do must consider, in general, a multitude of aspects related to the current and future state of the whole system, including the robot, the user, and the pills. In my work, I propose a predictive model of opportunity informing the decision of how to act to satisfy certain goals. This is reminiscent of the subject of planning. However, my aim is to not only enable the robot to autonomously select actions to achieve given goals, as done in planning, but also to infer the goals themselves. In line with [Hawes, 2011; Beaudoin, 1994; Ghallab et al., 2014; Pollack and Horty, 1999] I identify the need for a deliberative process independent from planning to realize this and refer to it as goal autonomy here. My aim is to provide a general solution to the problem of proactiveness in robots. To this means, I introduce the following concepts. Opportunity relates current and future states, courses of action and desirable states: an action is an opportunity if it keeps the system in desired states, and if the robot has the ability to enact it. Equilibrium is defined as the absence of opportunities. The claim of my thesis is that a proactive robot should have the persistent meta-goal to maintain equilibrium. Hence, an equilibrium maintenance algorithm closes the loop between desirable states and plan execution by continuously evaluating potential opportunities and deciding which ones to act upon. 2 Contributions