This paper proposes a real-time balance control technique that can be implemented to bipedal robots (exoskeletons, humanoids) whose ankle joints are powered via variable physical stiffness actuators. To achieve active balancing, an abstracted biped model, torsional spring-loaded flywheel, is utilized to capture approximated angular momentum and physical stiffness, which are of importance in postural balancing. In particular, this model enables us to describe the mathematical relation between Zero Moment Point and physical stiffness. The exploitation of variable physical stiffness leads to the following contributions: i) Variable physical stiffness property is embodied in a legged robot control task, for the first time in the literature to the authors' knowledge. ii) Through experimental studies conducted with our bipedal exoskeleton, the advantages of variable physical stiffness strategy are demonstrated with respect to the optimal constant stiffness strategy. The results indicate that the variable stiffness strategy provides more favorable results in terms of external disturbance dissipation, mechanical power reduction, and ZMP/CoM position regulation.

Abstract-This paper presents a wearable upper body exoskeleton system with a model based compensation control framework to support robot-aided shoulder-elbow rehabilitation and power assistance tasks. To eliminate the need for EMG and force sensors, we exploit off-the-shelf compensation techniques developed for robot manipulators. Thus target rehabilitation tasks are addressed by using only encoder readings.A proof of concept evaluation was conducted with 5 able-bodied participants. The patient-active rehabilitation task was realized via observer-based user torque estimation, in which resistive forces were adjusted using virtual impedance. In the patient-passive rehabilitation task, the proposed controller enabled precise joint tracking with a maximum positioning error of 0.25 degrees. In the power assistance task, the users' muscular activities were reduced up to 85% while exercising with a 5 [kg] dumbbell. Therefore, the exoskeleton system was regarded as being useful for the target tasks; indicating that it has a potential to promote robot-aided therapy protocols.

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.978-1-4799-6934-0/14/$31.00 ©2014 IEEE

This paper introduces a position-based compliance control algorithm that can be implemented in a lower extremity exoskeleton-supported paraplegia walking task, in which upper body has to be utilized to maintain the overall balance. In order to reduce the upper body effort required during the task, the controller is designated to be capable of managing the position/force trade-off in conjunction with an active admittance regulator scheme. In the case of no force errors, the controller prioritizes position tracking in a way to achieve walking support. Once the force error increases (e.g., ground reaction force peaks, unexpected disturbances, stepping on an object, etc.) the position reference is updated in accordance with the force constraints and active admittance characteristics. By the virtue of this strategy, the human-robot system exhibits enhanced environmental interaction capabilities; therefore, the subject can maintain the overall balance with relatively less upper body effort while walking. Implementing the proposed method, we conducted robot-assisted walking experiments on 4 able-bodied subjects with different body mass index levels and genders. Subjects were instructed to be in passive mode. In addition, walking with severe obstacles was also experimented on a single able-bodied subject. In conclusion, the proposed method enabled us to yield enhanced walking performance comparing to classical rigid position control scheme; indicating that it could potentially introduce a compliant locomotion control alternative for the paraplegia walking support task with a comparatively less amount of upper body effort requirements.

This paper is aimed at describing a technique to compensate undesired yaw moment, which is inevitably induced about the support foot during single support phases while a bipedal robot is in motion. The main strategy in this method is to rotate the upper body in a way to exert a secondary moment that counteracts to the factors which create the undesired moment. In order to compute the yaw moment by considering all the factors, we utilized Eulerian ZMP Resolution, as it is capable of characterizing the robot's rotational inertia, a crucial component of its dynamics. In doing so, intrinsic angular momentum rate changes are smoothly included in yaw moment equations. Applying the proposed technique, we conducted several bipedal walking experiments using the actual bipedal robot CoMan. As the result, we obtained 61% decrease in undesired yaw moment and 82% regulation in yaw-axis deviation, which satisfactorily verify the e±ciency of the proposed approach, in comparison to o®-the-shelf techniques.

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Abstract-We present a learning-based approach for minimizing the electric energy consumption during walking of a passively-compliant bipedal robot. The energy consumption is reduced by learning a varying-height center-of-mass trajectory which uses efficiently the robot's passive compliance. To do this, we propose a reinforcement learning method which evolves the policy parameterization dynamically during the learning process and thus manages to find better policies faster than by using fixed parameterization. The method is first tested on a function approximation task, and then applied to the humanoid robot COMAN where it achieves significant energy reduction.

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