Humans have remarkable sensorimotor learning capabilities. Our research integrates computational means and robotic platforms to investigate the principles underlying humans’ ability to quickly learn complex motor tasks. More specifically, we focus on the following questions:

(i) How can humans learn neuromotor primitives to carry out complex tasks?
(ii) How does learning explore and enable adaptation to individual biomechanics properties (e.g., mechanical impedance)? To answer these questions, experimental tools, such as EMGs, and software, such as OpenSim, are utilized to collect and analyze data from humans when performing tasks.

By extracting the analytic principles, the computational models (e.g., sensorimotor learning and neuromechanics control) are developed and tested both in simulated and physical bio-robotic platforms, such as human-like robot arms. The robot platforms and the controllers in turn serve as inspiring means to stimulate new experiments with human motor control tasks.

Taken together, human robotics paves a forward way to close the human-robot research loop. The results can contribute to the next generation exoskeleton augmentation technology, e.g., lightweight designs, user-friendly control, and seamless collaboration.