Advances in human sensing and machine learning are paving the way for new applications of robotics in sports and fitness, making skill coaching smarter, easier and more accessible. Physical and social human robot interaction in particular has received special attention as a feedback mechanism for human performance augmentation. A core challenge in deploying robots that interact physically with humans in dynamic environments such as sports, relates to modeling human skills and designing appropriate interaction schemes. We present the first ML-based HRI platform for physical robot to human skill coaching in real-time in Martial Arts which can be extended to various sports. Our system comprises of the Sawyer robot, our specially developed IoT katana and a skill-training program for the Martial Art of Iaido. We built and deployed in real-time a ML-based Iaido strike recognition model trained on expert and beginner data, and achieved accuracies ranging between 94.8% and 99.97%. We assessed the system’s effectiveness in coaching skills through robot interaction in a sparring experiment and a survey involving 12 participants practicing key Iaido techniques with guided training from Sawyer. Our results demonstrated improvement in all participants’ Iaido strike skill after training with Sawyer, and they responded positively to robot-assisted skill coaching.

Human and machine interaction is shaping the future of work in a growing body of applications ranging from big data analysis to healthcare. While many employer-centered solutions have emerged in recent years in areas such as crowd-sourcing and big-data analysis, worker-centered approaches have received less attention. In this work, we explore a worker-centered application of Human-Robot Interaction (HRI) for improving human well-being at work through actively sensing and recognising sitting posture habits. We present a platform for data-driven human sitting posture training based on adaptive HRI feedback using a novel robot called Owro. Owro is an owl-shaped emotive desktop robot that connects with the LifeChair, which is an IoT cushion for sitting posture training. The robot displays various emotive feedback to correct poor sitting habits based on the actively recognised human sitting posture. Our results demonstrated that the HRI feedback was effective in improving sitting posture by significantly increasing the percentage of time spent sitting upright from 15.62% to 47.96%, significantly reducing the percentage of amount of time spent slouching from 84.38% to 52.04%, and promoting a healthier back pressure distribution. We also show preliminary survey results on the human perception of the HRI feedback using Owro.