Thomas, Gray Assistant Professor

While advances in lightweight, backdrivable hardware are pushing wearable and other physically-interactive robots toward applications in everyday life, the way these robots are controlled today limits them in a fundamental way. From assistive lower-body exoskeletons to interactive co-bot arms, today's controllers rely on knowledge of the task (e.g. walking or assembling furniture) to make assumptions about what the operator wants and will do. Although the goal of these robots is ultimately to achieve the breadth of tasks and fluidity of transitions that a person has, the field has adopted a paradigm in which controllers are designed to ignore transitions. In so doing, we have left the problem of transitions to a high-level AI classifier, without necessarily considering the responsiveness, stability, or reliability of the classifier's feedback interaction with the wearer. Stated simply, real-time controllers are ignoring the human's input, when it should actually be the most important input. Fully exploiting the frameworks of estimation and control theory, on the other hand, offers the potential to allow humans to control robots directly, through physical interaction that amplifies their intent--empowering people with the strength of machines. The Human-Empowering Robotics and Control (HERC) Lab in the Mike J. Walker '66 Department of Mechanical Engineering at Texas A&M University aims to bridge this gap between estimation and control theory and physically interactive robotics to pursue fully-task-invariant feedback systems that augment human capabilities.