Exoskeleton robots demonstrate promise in their application in assisting or enhancing human physical capacity. Joint muscular torques (JMT) reflect human effort, which can be applied on an exoskeleton robot to realize an active power-assist function. The estimation of human JMT with a wearable exoskeleton is challenging. This paper proposed a novel human lower limb JMT estimation method based on the inverse dynamics of the human body. The method has two main parts: the inverse dynamic approach (IDA) and the sensing system. We solve the inverse dynamics of each human leg separately to shorten the serial chain and reduce computational complexity, and divide the JMT into the mass-induced one and the foot-contact-force (FCF)-induced one to avoid switching the dynamic equation due to different contact states of the feet. An exoskeleton embedded sensing system is designed to obtain the user’s motion data and FCF required by the IDA by mapping motion information from the exoskeleton to the human body. Compared with the popular electromyography (EMG) and wearable sensor based solutions, electrodes, sensors, and complex wiring on the human body are eliminated to improve wearing convenience. A comparison experiment shows that this method produces close output to a motion analysis system with different subjects in different motion.

This article studied a learning-based approach to learn grasping policies from teleoperated human demonstrations, which can achieve adaptive grasping using three different neural network (NN) structures. To transfer human grasping skills effectively, we used multisensing state within a sliding time window to learn the state–action mapping. By teleoperating an anthropomorphic robotic hand using human hand tracking, we collected training datasets from representative grasping of various objects, which were used to train grasping policies with three proposed NN structures. The learned policies can grasp objects with varying sizes, shapes, and stiffness. We benchmarked the grasping performance of all policies, and experimental validations showed significant advantages of using the sequential history states, compared to the instantaneous feedback. Based on the benchmark, we further validated the best NN structure to conduct extensive experiments of grasping hundreds of unseen objects with adaptive motions and grasping forces.

A novel balance assistance control strategy of a hip exoskeleton robot was proposed in this paper. The organic fusion of the human balance assessment and the exoskeleton balance assistance control strategy are the assurance of balance recovery. However, currently there are few human balance assessment methods that are suitable for detecting balance loss during standing and walking, and very little research has focused on exoskeleton balance recovery control. In this paper, a single step balance assessment method was proposed first, and then based on this method an "assist-as-needed" balance assistance control strategy was established. Finally, the exoskeleton balance assistance control experiment was carried out. The experiment results verified the effectiveness of the single balance assessment method and the active balance assistance control strategy.

The lower extremity exoskeleton is a device for auxiliary assistance of human movement. The interaction performance between the exoskeleton and the human is determined by the lower extremity exoskeleton’s controller. The performance of the controller is affected by the accuracy of the dynamic equation. Therefore, it is necessary to study the dynamic parameter identification of lower extremity exoskeleton. The existing dynamic parameter identification algorithms for lower extremity exoskeletons are generally based on Least Square (LS). There are some internal drawbacks, such as complicated experimental processes and low identification accuracy. A dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by Recursive Least Square (RLS) is developed in this investigation. The developed algorithm is named RLS-PSO. By defining the search space of PSO, RLS-PSO not only avoids the convergence of identified parameters to the local minima, but also improves the identification accuracy of exoskeleton dynamic parameters. Under the same experimental conditions, the identification accuracy of RLS-PSO, PSO and LS was quantitatively compared and analyzed. The results demonstrated that the identification accuracy of RLS-PSO is higher than that of LS and PSO.

The goal of this study is to present a force control scheme for quadrupedal locomotion by adopting observations of biological motor behavior. Specifically, based on the equilibrium point hypothesis, we set up a bio-inspired equilibrium point controller in Cartesian space. The proposed equilibrium point controller modifies the equilibrium point trajectories appropriately over time from two perspectives, which can ensure stable interactions and system equilibrium. One perspective is directly compensating for the posture angle error based on torso posture compensation. The other is based on the foot force tracking algorithm and admittance model. The main contribution of this study is to show how the equilibrium point controller realizes dynamic balance with no input about inertial parameter identification for the robot or terrain information estimation. Overall, the equilibrium point control scheme is simplified for stability problems and is easy to use in practice. Finally, we carried out a series of simulations and experiments to evaluate the effectiveness of the equilibrium point control algorithm. The results demonstrate that the proposed controller may improve dynamic stability and realize compliance performance.