This paper presents a novel approach for designing a robotic orthosis controller considering physical human-robot interaction (pHRI). Computer simulation for this human-robot system can be advantageous in terms of time and cost due to the laborious nature of designing a robot controller that effectively assists humans with the appropriate magnitude and phase. Therefore, we propose a two-stage policy training framework based on deep reinforcement learning (deep RL) to design a robot controller using human-robot dynamic simulation. In Stage 1, the optimal policy of generating human gaits is obtained from deep RL-based imitation learning on a healthy subject model using the musculoskeletal simulation in OpenSim-RL. In Stage 2, human models in which the right soleus muscle is weakened to a certain severity are created by modifying the human model obtained from Stage 1. A robotic orthosis is then attached to the right ankle of these models. The orthosis policy that assists walking with optimal torque is then trained on these models. Here, the elastic foundation model is used to predict the pHRI in the coupling part between the human and robotic orthosis. Comparative analysis of kinematic and kinetic simulation results with the experimental data shows that the derived human musculoskeletal model imitates a human walking. It also shows that the robotic orthosis policy obtained from two-stage policy training can assist the weakened soleus muscle. The proposed approach was validated by applying the learned policy to ankle orthosis, conducting a gait experiment, and comparing it with the simulation results.

While controlling a lower limb exoskeleton providing walking assistance to wearers, the walking terrain is an important factor that should be considered for meeting performance and safety requirements. Therefore, we developed a method to estimate the slope and elevation using the contact points between the limb exoskeleton and ground. We used the center of pressure as a contact point on the ground and calculated the location of the contact points on the walking terrain based on kinematic analysis of the exoskeleton. Then, a set of contact points collected from each step during walking was modeled as the plane that represents the surface of the walking terrain through the least-square method. Finally, by comparing the normal vectors of the modeled planes for each step, features of the walking terrain were estimated. We analyzed the estimation accuracy of the proposed method through experiments on level ground, stairs, and a ramp. Classification using the estimated features showed recognition accuracy higher than 95% for all experimental motions. The proposed method approximately analyzed the movement of the exoskeleton on various terrains even though no prior information on the walking terrain was provided. The method can enable exoskeleton systems to actively assist walking in various environments.

Lower extremity assistive devices (LEADs) have been developed in various fields, such as rehabilitation, military, and industry, in the form of exoskeleton robots or treadmills, and most of them are aimed at supporting muscle strength. However, unlike the aforementioned fields, the objective of LEADs developed in the space field is to provide resistance training to prevent muscle atrophy, which is a problem that arises in astronauts during long-duration space flights. Because the purpose of a LEAD is different from those of systems that are intended for use under Earth gravity (1 g) condition, other factors should be considered for the system design. In this study, the appropriate locations and types of actuators for reproducing the kinematics and muscle-related state variables observed in 1 g normal walking in a microgravity environment were proposed, and the corresponding control inputs obtained using a dynamic optimization simulation method. In detail, two actuation types were proposed, considering the characteristics of a microgravity environment in which both the magnitude of the gravitational acceleration and the ground reaction force were decreased. Moreover, by using the proposed actuating system, the control inputs required to track kinematics data and muscle activity were obtained. A human lower-limb model, with six degrees of freedom, i.e., an 18-muscle model with the pelvis fixed, was used with ideal actuators to apply torques or forces to joints or soles. Dynamic optimization was performed to solve these problems using direct collocation with OpenSim and MATLAB. Using the two proposed types of actuation, the results agreed with the kinematics and muscle activity of 1 g normal walking, and the total joint torques by the muscles also exhibited similar curves to that of the net joint torques under 1 g normal walking. The results of this study suggested an actuation method and its control input that can be used in the design of a LEAD for resistance training in microgravity.