SUMMARY

The goal of this research is to develop theories and tools to understand the mechanisms of neuromotor adaptation in human-robot physical interaction, in order to improve the stability and performance of the interaction. Human power-assisting systems (e.g., intelligent physical assistance or iPA used in manufacturing, building, construction, healthcare, logistics, and other industries) require physical contact between the operator and machine, creating a coupled dynamic system. This dynamic coupling has been shown to be prone to instability and performance degradation due to a change in human stiffness; when instability is encountered, a human operator often attempts to control the oscillation by stiffening their arm, which leads to a stiffer system with more instability. Robot co-worker controllers must account for this issue. The proposed research will:
- Provide motivation for the addition of operator physiological data as a means to improve the control of haptic assist devices.
- Present a method for modeling operator endpoint stiffness based on measured muscle activation levels of a select group of upper-limb muscles.
- Present a data-driven optimization method for determining impedance parameters for multi-degree-of-freedom (multi-DoF) haptic devices.
- Propose an integrated data-driven control approach that bypasses the need for a human operator model.

The project will establish control algorithms for robot co-workers that proactively adjust the contact impedance between the operator and robotic manipulator for achieving higher performance and stability.