  Robotic exoskeletons are exciting technologies for augmenting human mobility.
However, designing such a device for seamless integration with the human user
and to assist human movement still is a major challenge. This paper aims at
developing a novel data-driven solution framework based on reinforcement
learning (RL), without first modeling the human-robot dynamics, to provide
optimal and adaptive personalized torque assistance for reducing human efforts
during walking. Our automatic personalization solution framework includes the
assistive torque profile with two control timing parameters (peak and offset
timings), the least square policy iteration (LSPI) for learning the parameter
tuning policy, and a cost function based on transferred work ratio. The
proposed controller was successfully validated on a healthy human subject to
assist unilateral hip extension in walking. The results showed that the optimal
and adaptive RL controller as a new approach was feasible for tuning assistive
torque profile of the hip exoskeleton that coordinated with human actions and
reduced activation level of hip extensor muscle in human.
