Iterative Repair of Social Robot Programs from Implicit User Feedback via Bayesian Inference

Abstract

Creating natural and autonomous interactions with social robots requires rich, multi-modal sensory input from the user. Writing interactive robot programs that make use of this input can demand tedious and error-prone tuning of program parameters, such as tuning thresholds on noisy sensory streams for detecting whether the robot's user is engaged or not. This tuning process dealing with low-level streams and parameters makes programming of social robots time-consuming and inaccessible for people who could benefit the most from unique use cases of social robots. To address this challenge, we propose the use of iterative program repair, where programmers create an initial program sketch in our new Social Robot Program Transition Sketch Language (SoRTSketch), a domain-specific language that supports expressing uncertainties related to thresholds in transition functions. The program is then iteratively repaired using Bayesian inference based on corrections of interaction traces that are either provided by the programmer or derived from implicit feedback given by the user during the interaction.