This work presents a vertical center of mass (CoM) trajectory generation approach based on preview control for quadruped robots to accomplish agile locomotion tasks such as jumping and jogging. Conventionally, preview control is implemented to walking planning with constant CoM height, since this condition renders the pendulum equation linear. In this matter, we show that the same implementation can be done for varying CoM height, while the pendulum frequency is constant. To this end, we compactly derived the infinite horizon and finite horizon time-invariant linear quadratic regulator solutions for potential real-time applicability. The proposed trajectory generation approach was tested and compared with conventional approaches via a set of simulation experiments, where we used a realistic model of our quadruped robot Kara. As a result, we obtained dynamically-balanced, coherent, and continuous jumping and spot-jogging behavior, adequately confirming our approach.

Exoskeletons are increasingly helping people with different applications. Regardless of what they were built for, exoskeletons have a common discomfort problem from the misalignment of robot and human joints. In this paper, a fixation design for a lower extremity exoskeleton is presented. A method was proposed to determine necessary passive degrees of freedom of the designed brace system and to identify the parameters affecting interaction forces and moments between human and exoskeleton. The proposed method was validated by analyzing the human-machine interface statically and dynamically. The results show that the problem of undesired interaction forces due to misalignment may be solved theoretically with the proposed design.

This article presents a locomotion controller for lower limb exoskeletons so as to enable the combined robot and user system to exhibit compliant walking characteristics when interacting with the environment. This is of critical importance to reduce the excessive ground reaction forces during the walking task execution with the aim of improved environmental interaction capabilities. In robot-aided walking support for paraplegics, the user has to actively use his/her upper limbs via crutches to ensure overall balance. By virtue of this requisite, several issues may particularly arise during touchdown instants, e.g., upper body orientation fluctuates, shoulder joints are subject to excessive loading, and arms may need to exert extra forces to counterbalance these effects. In order to reduce the upper body effort via compliant locomotion, the controller is designed to manage the force/position tradeoff by using an admittance controller in each joint. For proof of concept, a series of exoskeleton-aided walking experiments were conducted with the participation of nine healthy volunteers, four of whom additionally walked on an irregular surface for further performance evaluation. The results suggest that the proposed locomotion controller is advantageous over conventional high-gain position tracking in decreasing undesired oscillatory torso motion and total arm force, adequately reducing the required upper body effort.

This paper presents a method that aims to reduce the complex cabling problem in robotics applications, so as to prevent difficulties in troubleshooting processes and failures in case of cable breaks. Examples in the literature have proven the reliability of the Bluetooth Low Energy (BLE) based communication systems and indicated that it could be employed in sensor data transfer applications. Therefore, our solution to the aforementioned issue is independent joint modules that comprise a motor driver, a microcontroller for running servo control loops, and a BLE device that enables the module to communicate with the main processor of the robot without the need of bulky cabling. Target applications for this method are mobile robots that encapsulate onboard processors and actuators that are deployed within a short range.

In this paper, a method to control a manipulator using force-induced trajectory is proposed. The trajectory is learned from an operator doing the polishing task using a tool attached to the robot's end effector. The learning process is performed by a deep neural network which is designed and trained to generate a force profile according to the states (joints' positions and velocities). The admittance control technique is utilized to make the manipulator compliant to the operator movements in the teaching mode. Spring-Damper system along with Inertia-Damper system have been studied to impose the relationship between the operator's applied force and the reaction of the manipulator. The universal robot (UR5) aside with a force sensor (OptoForce) are used to run the experiment. Robot Operation System (ROS) is used to accomplish the task in real time. The polishing task is learned and achieved by the robot itself, and the force trajectories are better followed using the Inertia-Damper system as the admittance controlling scheme.

This paper proposes a novel controller framework for antagonistically driven pneumatic artificial muscle (PAM) actuators. The proposed controller can be stably configured in both torque-stiffness control and position-stiffness control modes. Three contributions are sequentially presented in constructing the framework: 1) A PAM force feedback controller with guaranteed stability is synthesized in a way so as to contend with nonlinear PAM characteristics; 2) a mathematical tool is developed to compute reference PAM forces, for a given set of desired joint torque and joint stiffness inputs; and 3) on top of the torque controller, a position control scheme is implemented and its stability analysis is given in the sense of Lyapunov. In order to test the controller framework, an extensive set of experiments are conducted using an actuator that is constructed using two antagonistically coupled PAMs. As a result, the actuator exhibits satisfactory tracking performances concerning both torque-stiffness control and position-stiffness control modes.

This paper presents a biologically-inspired real-time balance recovery control strategy that is applied to a lower body exoskeleton with variable physical stiffness actuators at its ankle joints. For this purpose, a torsional spring-loaded flywheel model is presented to encapsulate both approximated angular momentum and variable physical stiffness, which are crucial parameters in describing the postural balance. In particular, the incorporation of physical compliance enables us to provide three main contributions: i) A mathematical formulation is developed to express the relation between the dynamic balance criterion ZMP and the physical ankle joint stiffness. Therefore, balancing control can be interpreted in terms of ankle joint stiffness regulation. ii) ‘Variable physical’ stiffness is utilized in the bipedal robot balance control task for the first time in the literature, to the authors' knowledge. iii) The variable physical stiffness strategy is compared with the optimal constant stiffness strategy by conducting experiments on our exoskeleton robot. The results indicate that the proposed method provides a favorable balancing control performance to cope with unperceived perturbations, in terms of center of mass position regulation, ZMP error and mechanical power.

This paper describes a novel controller that is capable of simultaneously controlling torque and variable stiffness in real-time, for actuators with antagonistically driven pneumatic artificial muscles (PAMs). To this end, two contributions are presented: i) A stable force feedback controller that can cope with inherent PAM nonlinearities is synthesized using the dissipativity theory, for each PAM unit. ii) On top of this force feedback controller, a mathematical formulation is developed to compute reference force inputs that correspond to desired joint torque and joint stiffness inputs, concerning both agonist and antagonist PAMs. This strategy enables us to introduce real-time sensory feedback; torque and stiffness control is addressed by means of PAM force feedback control with guaranteed stability. To validate the proposed control scheme, a series of experiments were conducted on an experimental setup. As the result, the controller exhibited favorable torque and stiffness tracking in real-time, demonstrating that it could meet the performance criteria to power exoskeleton systems.

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 live 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°. 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.

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.

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.

This paper is authored to describe a control framework that is designated for realizing cyclic, actively-compliant and dynamically-balanced jumping and trotting quadruped locomotion over rough terrain. In order to succeed in exhibiting such locomotion abilities, two controllers are synthesized: i) Active Compliance Control via force feedback, ii) Angular Momentum Control via gyro sensing. The first controller computes the joint displacements that are associated with ground reaction force errors, using Jacobian transpose and admittance blocks. Together with position constraints, these joint displacements are simultaneously fed-back to local servo controllers; allowing the robot to perform the given locomotion task in an actively-compliant manner. The second controller, in the meantime, evaluates gyro sensor information to calculate the required compensation torque about center of mass, which is necessary to regulate upper torso rotational motion. Afterwards, it updates the orientation input in accordance with this compensation torque. Using the proposed framework, the overall control performance is tested via cyclic jumping and trotting motion experiments, conducted over rough terrain with a stiff-by-nature quadruped robot. Results turn out to be positive; the robot demonstrates successful jumping and trotting cycles in a repetitive, actively-compliant and dynamically-balanced fashion.

This paper presents a trajectory generator and an active compliance control scheme, unified in a framework to synthesize dynamic, feasible and compliant trot-walking locomotion cycles for a stiff-by-nature hydraulically actuated quadruped robot. At the outset, a CoP-based trajectory generator that is constructed using an analytical solution is implemented to obtain feasible and dynamically balanced motion references in a systematic manner. Initial conditions are uniquely determined for symmetrical motion patterns, enforcing that trajectories are seamlessly connected both in position, velocity and acceleration levels, regardless of the given support phase. The active compliance controller, used simultaneously, is responsible for sufficient joint position/force regulation. An admittance block is utilized to compute joint displacements that correspond to joint force errors. In addition to position feedback, these joint displacements are inserted to the position control loop as a secondary feedback term. In doing so, active compliance control is achieved, while the position/force trade-off is modulated via the virtual admittance parameters. Various trot-walking experiments are conducted with the proposed framework using HyQ, a ~ 75kg hydraulically actuated quadruped robot. We present results of repetitive, continuous, and dynamically equilibrated trot-walking locomotion cycles, both on level surface and uneven surface walking experiments.

It is known that bipedal robots with passive compliant structures have obvious advantages over stiff robots, as they are able to handle the potential energy management. Therefore, this paper is aimed at presenting a jumping pattern generation method that takes advantage of this property via the utilization of the base resonance frequency, which is of special importance. To begin with, the resonance frequency is determined through a system identification procedure on our actual robot. Consequentially, the vertical component of the CoM is generated via a periodic function in which the resonance frequency is employed. The horizontal component of the CoM is obtained using the ZMP criterion to guarantee the dynamic balance. Having obtained the necessary elements of the CoM trajectory within an analytical manner, joint motions are computed with the help of translational and angular momenta constraints. In order to validate the method, two legged jumping experiments are conducted on our actual compliant robot. In conclusion, we observed repetitive, continuous, and dynamically equilibrated jumping cycles with feasible landing phases.

A summary of the state-of-the-art reinforcement learning in robotics is given, in terms of both algorithms and policy representations. Numerous challenges faced by the policy representation in robotics are identified. Two recent examples for application of reinforcement learning to robots are described: pancake flipping task and bipedal walking energy minimization task. In both examples, a state-of-the-art Expectation-Maximization-based reinforcement learning algorithm is used, but different policy representations are proposed and evaluated for each task. The two proposed policy representations offer viable solutions to four rarely-addressed challenges in policy representations: correlations, adaptability, multi-resolution, and globality. Both the successes and the practical difficulties encountered in these examples are discussed.

An automated approach is proposed which can analyze ground reaction force data from bipedal walking robots and humans. The input of the automated analysis is the raw data from force sensors mounted in the feet of a robot. The output is detailed information, such as detected single support, double support, and swing phases, their durations, timings of events like heel strikes, properties of the phase transitions and of the robot itself. The proposed approach is generic, parameter-free, model-free, robust, computationally efficient, and applicable for real-time use during walking. It can detect early indications of instability that could lead to a fall of the robot. Three real-world experiments are presented: with a compliant bipedal robot, with a stiff humanoid robot, and with a human subject.

This paper is aimed at presenting a framework that consists of a pattern generator and a controller, which are combined together to realize continuous and dynamically equilibrated running motion on a 4-link 3-DoF one-legged robot with no passively compliant elements. Initially, we make use of a pattern generator to synthesize dynamically-consistent running trajectories in which the rotational inertia and the associated angular momentum term are characterized. As for the controller, ground reaction force constraints are imposed to the system indirectly. For this purpose, joint torque values that are corresponding to horizontal and vertical force errors are computed. Subsequently, they are inserted to an admittance filter block to obtain the associated joint displacements. These joint displacements are then fed-back to local servo controllers to implement indirect force feedback control in an actively compliant manner. Additionally, friction compensation and foot orientation controller blocks are added to enhance the system performance. In order to validate the method, running experiments are conducted on the actual one-legged robot. As the result, we satisfactorily obtained continuous, dynamically equilibrated and repetitive running cycles.

This paper aims to present a technique to generate feasible and dynamically equilibrated ZMP-based center of mass trajectories that can be applied to bipedal robots. In this regard, we utilize the ZMP concept in the spherical coordinate frame so that we can fully exploit its properties as this strategy enables us to efficiently combine intrinsic angular momentum rate change terms with inertial force terms. That being said, this strategy has certain advantages: 1) In the case of bipedal walking, undesired torso angle fluctuations are more restrainable compared with other methods, in which angular momentum information is omitted or zero-referenced. 2) Composite rigid body inertia, which is a multibody property of robot dynamics, can be characterized during the trajectory generation task. Thus, relatively more dynamically consistent trajectories may be obtained. 3) The motion interference between the sagittal and lateral planes is naturally included. In this paper, we mainly investigate the first two advantages. Applying the method described above, we conducted bipedal walking experiments on our actual bipedal robot MARI-3. As the result, we obtained repetitive, continuous, and dynamically equilibrated walking cycles, in which undesired torso angles were well suppressed. Furthermore, ZMP error tends to decrease since inertial parameters of the robot are characterized. In conclusion, the method is validated to be efficient in inducing less ZMP error and in suppressing undesired torso angle variations, compared with both flywheel-superimposed and conventional ZMP-based trajectory generation methods.

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.

This paper is aimed at implementing Eulerian ZMP Resolution method to bipedal walking pattern generation. The main strategy in this method is to ensure the dynamic balance by generating feasible ZMP-based CoM trajectories. For this purpose, we employ ZMP equations in spherical coordinates, so that the intrinsic angular momentum rate change about center of mass is included explicitly in a natural way. This fact results in two merits: 1) Undesired torso angle fluctuation and body twists are expected to be more restrainable comparing to other methods in which intrinsic angular momentum information is ignored or zero-referenced. 2) The interference between motions in sagittal and lateral planes can be extracted. In this article, we mainly investigate the first merit and briefly discuss about the second merit. Applying the aforementioned technique, Eulerian ZMP Resolution, we simulated bipedal walking on a 3-D dynamic simulator. Secondarily, we conducted bipedal walking experiments on the actual bipedal robot. In conclusion, we obtained dynamically equilibrated bipedal walking cycles, which satisfactorily verify the efficiency of Eulerian ZMP Resolution technique over conventional methods.

This paper is presenting a method to generate real-time running and jumping trajectories that can be applied to bipedal humanoid robots. The proposed method is based on maintaining the overall dynamic balance by using the ZMP stability criterion throughout support phases. To be able to reach this goal, we utilize ZMP equations in spherical coordinates, so that the rate change of angular momentum terms in ZMP equations are included naturally by using Eulerian equations of motion for unsymmetrical bodies. Thus, undesired torso angle fluctuation is expected to be more restrainable comparing to other methods in which angular momentum information is ignored or zero-referenced. Applying the aforementioned technique, we primarily simulated running motion on a dynamic 3-D simulator. Secondarily, one-legged jumping experiments were conducted on the actual bipedal robot. In conclusion, we obtained repetitive and successful running and jumping cycles which satisfactorily verify the proposed method.