Improving Work Skills in People with Disabilities in a Long-Term, In-Field Study on Robotic Tutors

Abstract Inclusion of people with disabilities in the open labor market using robotic assistance is a promising new and important field of research, albeit challenging. People with disabilities are severely underrepresented in the open labor market, although inclusion adds significant value on both financial and social levels. Here, collaborative industrial robots offer great potential for support. This work conducted a month-long, in-field user study in a workshop for people with disabilities to improve learning progress through collaboration with an innovative intelligent robotic tutoring system. Seven workers with a wide variety of disabilities solved assembly tasks while being supervised by the system. In case of errors or hesitations, different modes of assistance were automatically offered. Modes of assistance included robotic pointing gestures, speech prompts, and calling a supervisor. Which assistance to offer the different participants during the study was personalized by a shared policy using reinforcement learning. Here, new, non-stationary Contextual Multi-Armed Bandit algorithms were developed during the prior simulation-based study planning to include the workers contextual information. Pioneering results were obtained in three main areas. The participants significantly improved their skills in terms of time required per task. The algorithm learned within only one session per participant which modes of assistance were preferred. Finally, a comparison between simulation and re-simulation, including the study results, revealed the underlying basic assumptions to be correct but individual variation led to strong performance differences in the real-world setting. Looking ahead, the innovative system developed could pave the way for many people with disabilities to enter the open labor market.