Tatsuya Teramae

An electromyogram based force control coordinated in assistive interaction

This study proposes the design of electromyography (EMG)-based force feedback controller which explicitly considers human-robot interaction for the exoskeletal assistive robot. Conventional approaches have been only consider one-directional mapping from EMG to control input for assistive robot control. However, EMG and force generated by the assistive robot interfere each other, e.g., amplitude of EMG decreases if limb movements are assisted by the robot. In our proposed method, we first derive the nonlinear mapping from EMG signal to muscle force for estimating human joint torque, and convert it to assistive force using human musculoskeletal model and robot kinematic model. Additionally the feedforward interaction torque is feedback into torque controller to acquire the necessity loads. To validate the feasibility of the proposed method, assistive One-DOF system was developed as the real equipment and the simulator. We compared the proposed method with conventional approaches using both the simulated and the real One-DOF systems. As the result, we found that the proposed model was able to estimate the necessary torque adequately to achieve stable human-robot interaction.

Development of an upper limb exoskeleton powered via pneumatic electric hybrid actuators with bowden cable.

In this paper, we introduce our ongoing work on the development of an upper body exoskeleton robot, driven by a pneumatic-electric hybrid actuation system. Since the limb of an exoskeleton robot needs to have small inertia to achieve agility and safety, using a heavy actuator is not preferable. Furthermore, we need to use backdrivable actuators that can generate sufficiently large torques to support user movements. These two requirements may seem contradictory. In order to cope with this development problem, we use a hybrid actuation system composed of Pneumatic Artificial Muscles (PAMs) and small-size electromagnetic motors. Although we and other research groups have already presented the advantage of the hybrid actuation system, we newly propose the usage of Bowden cable in a hybrid actuator to transmit the force generated by the PAMs to joints of our exoskeleton robot so that we can design a compact upper limb with small inertia. In addition, small size electric motors are mechanically connected to joints in order to compensate uncertainty generated by the PAM dynamics and the Bowden cable. We demonstrate that the proposed joint is backdrivable with the capability of large torque generation for the gravity compensation task both in One-DOF system with a dummy weight and right arm of the upper body exoskeleton with a mannequin arm. We also show the right arm exoskeleton can be moved using a torque input, extracted from sensory information via a goniometer.

Learning assistive strategies for exoskeleton robots from user-robot physical interaction

Abstract Social demand for exoskeleton robots that physically assist humans has been increasing in various situations due to the demographic trends of aging populations. With exoskeleton robots, an assistive strategy is a key ingredient. Since interactions between users and exoskeleton robots are bidirectional, the assistive strategy design problem is complex and challenging. In this paper, we explore a data-driven learning approach for designing assistive strategies for exoskeletons from user-robot physical interaction. We formulate the learning problem of assistive strategies as a policy search problem and exploit a data-efficient model-based reinforcement learning framework. Instead of explicitly providing the desired trajectories in the cost function, our cost function only considers the user’s muscular effort measured by electromyography signals (EMGs) to learn the assistive strategies. The key underlying assumption is that the user is instructed to perform the task by his/her own intended movements. Since the EMGs are observed when the intended movements are achieved by the user’s own muscle efforts rather than the robot’s assistance, EMGs can be interpreted as the “cost” of the current assistance. We applied our method to a 1-DoF exoskeleton robot and conducted a series of experiments with human subjects. Our experimental results demonstrated that our method learned proper assistive strategies that explicitly considered the bidirectional interactions between a user and a robot with only 60 seconds of interaction. We also showed that our proposed method can cope with changes in both the robot dynamics and movement trajectories.

Estimating joint movements from observed EMG signals with multiple electrodes under sensor failure situations toward safe assistive robot control

In this paper, we propose an estimation method of human joint movements from measured EMG signals for assistive robot control. We focus on how to estimate joint movements using multiple EMG electrodes even under sensor failure situations. In real world applications, EMG sensor electrodes might become disconnected or detached from skin surfaces. If we consider EMG-based robot control for assistive robots, such sensor failures lead to significant errors in the estimation of user joint movements. To cope with these sensor failures, we propose a state estimation model that takes uncertain observations into account. Sensor channel anomalies are found by checking the covariance of the EMG signals measured by multiple EMG electrodes. To validate the proposed control framework, we artificially disconnect an EMG electrode or detach one side of an EMG probe from the skin surface during elbow joint movement estimation. We show proper control of a one-DOF exoskeleton robot based on the estimated joint torque using our proposed method even when one EMG electrode has a sensor problem; a standard method with no tolerability against uncertain observations was unable to deal with these fault situations. Furthermore, the errors of the estimated joint torque with our proposed method were smaller than the standard method or a method with a conventional sensor fault detection algorithm.

An EMG-Driven Weight Support System With Pneumatic Artificial Muscles

In this paper, we introduce our newly developed biosignal-based vertical weight support system that is composed of pneumatic artificial muscles (PAMs) and an electromyography (EMG) measurement device. By using our developed weight support system, assist force can be varied based on measured muscle activities; most existing systems can only generate constant assist forces. In this paper, we estimated knee and ankle joint torques from measured EMGs using floating base inverse dynamics. Knee and ankle joint estimated torques are converted to vertical forces by the kinematic model of a subject. The converted vertical forces are used as force inputs for the PAM actuator system. To validate our systems control performance, four healthy subjects performed a one-leg squat with his left leg while his right leg was assisted by our proposed system. We used the vertical force estimated from the measured EMG signals as a control input to the weight support system. We compared EMG magnitudes with four different experimental conditions: 1) normal two-leg squat; 2) one-leg squat without the assist system; 3) one-leg squat with EMG-based weight support; and 4) one-leg squat with constant force support. The EMG magnitude with the proposed weight support system was much closer to that with normal two-leg squat than that with one-leg squat without the assist system and than that with one-leg squat with constant force support.

Optimal control approach for pneumatic artificial muscle with using pressure-force conversion model

In this paper, we propose an optimal control framework for pneumatic actuators. In particular, we consider using Pneumatic Artificial Muscle (PAM) as a part of Pneumatic-Electric (PE) hybrid actuation system. An optimal control framework can be useful for PE hybrid system to properly distribute desired torque outputs to the actuators that have different characteristics. In the optimal control framework, the standard choice to represent control cost is squared force or torque outputs. However, since the control input for PAM is pressure rather than the force or the torque, we should explicitly consider the pressure of PAM as the control cost in an objective function of the optimal control method. We show that we are able to use pressure input as the control cost for PAM by explicitly considering the model which represents a relationship between the pressure input and the force output of PAM. We demonstrate that one-DOF robot with the PE hybrid actuation system can generate pressure-optimized ball throwing movements by using the optimal control method.

Style-phase adaptation of human and humanoid biped walking patterns in real systems

Several studies have been attempted on human walking assistance using exoskeleton robots. To achieve the effective walking assistance with a variety of user motions, the robot behaviors need to be coordinated with both predicted user motions and the environment spatiotemporally. In this paper, we study how movement prediction and temporal synchronization can be beneficial for walking assist exoskeletons using the framework of style-phase adaptive pattern generation [1]. In particular, we empirically investigate the following two issues: i) mutual synchronization between a human subject and a humanoid model through style-phase adaptation, and ii) using style-phase adaptation for walking assistance. We developed two experimental platforms for the two investigations and conducted subjective experiments. The experimental results suggest that visual feedback of the state of the humanoid model can enhance the mutual synchronization through style-phase adaptation, and the estimated style and phase can be useful to assist walking movement of the human subject.

Modeling and control of a Pneumatic-Electric hybrid system

We introduce our Pneumatic-Electric (PE) hybrid actuator model and propose to use the model to derive a controller for the hybrid actuation system by an optimal control method. Our PE hybrid actuator is composed of Pneumatic Artificial Muscle (PAM) and an electric motor. The PE hybrid actuator is light and can generate large torque. These properties are desirable for assistive devices such as exoskeleton robots. However, to maximally take advantage of PE hybrid system, we need to reasonably distribute necessary torque to these redundant actuators by properly taking distinctive characteristics of a pneumatic actuator and an electric motor into account. To do this, in this study, we use an optimal control method called iterative LQG to reasonably distribute the necessary torque to the PAM and the electric motor. The crucial issue to apply the optimal control method to the PE hybrid system is PAM modeling. We built a PAM model composed of three elements: 1) an (air)pressure-force conversion model, 2) a contraction rate model, 3) time delay of the air valve, and 4) the upper limit of force generation that depends on the contraction rate and the movable range. We apply our proposed method to a one degree of freedom (one-DoF) arm with PE hybrid actuator. The one-DoF arm successfully swing tasks 0.5 Hz, 2 Hz and 4 Hz and swing up and stability task by reasonably distributing necessary torque to the two different actuators in a simulated and a real environments.

EMG-Based Model Predictive Control for Physical Human–Robot Interaction: Application for Assist-As-Needed Control

In this letter, we propose an electromyography (EMG)-based optimal control framework to design physical human–robot interaction for rehabilitation and develop a novel assist-as-needed (AAN) controller based on a model predictive control (MPC) approach. To enhance the recovery of motor functions, encouraging the voluntary movements of patients is necessary while a therapist is assisting them. Therefore, in an AAN control framework, the robot only assists the deficient torque to generate a target movement. In our study, we first estimate the joint torque of a patient from measured EMG signals and then derive the deficient joint torque to generate the target movements by considering the patients estimated joint torque with an MPC method. Results showed that our proposed method adaptively derived the necessary torque to follow the target elbow joint trajectories based on the subjects voluntary movements.

Learning assistive strategies from a few user-robot interactions: Model-based reinforcement learning approach

Designing an assistive strategy for exoskeletons is a key ingredient in movement assistance and rehabilitation. While several approaches have been explored, most studies are based on mechanical models of the human user, i.e., rigid-body dynamics or Center of Mass (CoM)-Zero Moment Point (ZMP) inverted pendulum moECenter of Massdel, or only focus on periodic movements with using oscillator models. On the other hand, the interactions between the user and the robot are often not considered explicitly because of its difficulty in modeling. In this paper, we propose to learn the assistive strategies directly from interactions between the user and the robot. We formulate the learning problem of assistive strategies as a policy search problem. To alleviate heavy burdens to the user for data acquisition, we exploit a data-efficient model-based reinforcement learning framework. To validate the effectiveness of our approach, an experimental platform composed of a real subject, an electromyography (EMG)-measurement system, and a simulated robot arm is developed. Then, a learning experiment with the assistive control task of the robot arm is conducted. As a result, proper assistive strategies that can achieve the robot control task and reduce EMG signals of the user are acquired only by 30 seconds interactions.