This paper presents a novel under-actuated twofinger gripper that passively adapts to various environments and maintains its grip posture using a passive-locking mechanism. The proposed mechanism features fingers with three phalanges, each incorporating four-bar and eight-bar linkages arranged in parallel. These linkages perform crucial functions, including maintaining the grip angle and ensuring passive characteristics during pinch grips. Previous grippers with passive mechanisms and three-phalanx fingers faced issues with gripping instability, particularly when changes in the passive joint angle were caused by object inertia or external lateral forces. To address this problem, we propose a new passive-locking mechanism utilizing an eight-bar linkage. This innovative design is engineered to adapt to environmental conditions, establish a secure grip, and maintain the grip angle of the passive joint after the grip is achieved. To demonstrate the advantages of the proposed mechanism, this paper conducts a fingertip force vector analysis and a mobility analysis according to the pinch sequence. It also details the derivation process and principles of the mechanism. The gripper's operational range and gripping force are examined through kinematic analysis and verified by simulation. Furthermore, the study shows that the proposed mechanism effectively responds to environmental constraints, even in environments with obstacles surrounding the object. Comparative experiments with and without a contact bar indicate that the proposed gripper can stably secure an object in scenarios involving swing motions and external forces of approximately$5 N$.

This study proposes a novel 7-DOF robotic manipulator called CoSMo-Arm for high torque density multi-link robotic platform based on a concentrically stacked modular actuator (CoSMoA) and an extended coaxial spherical joint module (E-CoSMo) introduced in previous researches. The CoSMoA is an actuator module designed to improve thermal characteristics by stacking the motor actuator parts to share the heat dissipation device of the adjacent actuator module, thereby theoretically amplifying motor performance by approximately 3.2 times. The E-CoSMo is a parallel joint mechanism connected to the end of the CoSMoA to create point-centered rotational four degrees of freedom. This joint module has a large range of motion in specific rotational directions and maximum output of approximately up to four times to the actuator output in the specific workspace. The CoSMo-Arm is designed to take advantages of these novel concept modules, having a higher payload than its own weight. To verify the benefits of the proposed mechanism, we performed kinematic analysis and dynamics simulations. From experimental verifications of the real prototype, the feasibility and validity are confirmed as a multi-DOF robotic manipulator.

In this study, a novel four degrees of freedom (DoF) robotic link mechanism and its torque analysis based on extended coaxial spherical joint module (E-CoSMo) are introduced. The E-CoSMo is a previously developed 4-DoF point-centered rotational parallel mechanism with coaxial actuators. The use of the E-CoSMo on robotic system is expected to improve dynamic performance since all actuators are mounted on fixed base. Furthermore, output torque is amplified in a specific axial direction, due to a coaxial parallel mechanism. Torque analysis of the mechanism is derived based on kinematics of a virtual 4-DoF serial linkages and the E-CoSMo. A virtual force-based position control simulation is performed for verifying the torque analysis. Due to the valid torque analysis, the norm of position error is less than 0. 7 mm. It is confirmed that the output torque is amplified compare to actuator torques, which is a special feature of the E-CoSMo. Control experiments using prototype are also carried out to validate the feasibility.

In a human-robot interaction (HRI) device that performs physical collaboration operations in constant contact with the user, admittance control and impedance control are generally used. Since the two controllers exhibit opposite performances depending on the stiffness condition, controllers capable of dealing with various magnitudes of stiffness are required. As such, this study proposes hybrid control that adjusts the control distribution ratios of admittance control and impedance control based on the operating frequency analysis to react to the user intention and various stiffness conditions in real time. The proposed controller algorithm exhibited lower overshoot than impedance control in the step input response simulation, faster response speed compared to admittance control in the response simulation for 0-5 Hz input frequencies, and the smallest vibration magnitude and number of vibrations in the case of a virtual wall collision, resulting in improved performance compared to existing control methods.

In this study, we propose an extended coaxial spherical joint module (E-CoSMo) with three to four degrees of freedom (DOFs) for a multi-DOF robot platform. The E-CoSMo consists of a coaxial spherical parallel mechanism (CSPM) with three DOFs and one extended DOF based on a universal joint mechanism (UJM) coaxially connected to the CSPM. This structure enables the application of serial link configuration (such as shoulder-elbow) with wide and universal ROMs while allowing all four actuators to be placed in the base. This makes the inertia of the moving link part to be dramatically reduced and thus contributes to decreasing the mechanical impedance of the multi-DOF robot system. In addition, through the effective design of the coaxial spherical joint module, the output rotational torque in a specific axial direction reaches approximately three times then the torque of a single actuator. To optimally implement this, we applied an optimal design approach that considers the mechanical performance and design constraints. The mechanical impedance reduction effect through the proposed module is discussed. The feasibility of the E-CoSMo is also verified through a dynamic simulation. Finally, the proposed mechanism is verified using a fabricated prototype.

While the central pattern generator (CPG) controller based on biologically inspired neural oscillator networks is known to achieve repeatability and entrainment of rhythmic output motion, no systematic approach to implement the controller has been provided so far. In this study a series of schemes are introduced to tune the control parameters and to set up the network coupling of artificial neural oscillators combined with the virtual force control. Application of the schemes to the proposed controller for a three-link planar robot arm demonstrates that the optimized controller with proper neural oscillator networks can produce minimize the energy consumption with an enhanced-entrained motion with the change of a desired motion and interaction of unknown environments.

Recent progress in wearable exoskeleton robotics results from various needs of an aging society. This would spawn a vulnerable workforce in industry sectors to the near future. Thus, for the application to the industrial field, this work proposes an assistive robotic exoskeleton attached to the calf and thigh of the human leg to totally support a worker's body weight in stand-to-sit with the various seated positions. For this, in this work, a four-bar mechanism based various combinations of passive and active actuators are investigated. Finally the proposed system is composed of one rotary actuator and linear spring based on a four-bar mechanism, considering the high power efficiency for the weight and the mechanical solution to the backdrivability problem of a geared drive. From the extensive simulations, the basic actuation mechanism of the proposed assistive device is confirmed. Then it is verified from experimental results that the robotic exoskeleton can be used to aid who weighs maximum 85 kg in the arbitrary seated position and is actuated with nearly zero current consumption in the walking.

Biologically inspired control approaches have been attracted much attention as alternatives in recent time, for efficiently solving problems in controlling multi-DOF robotic systems, since most human beings or animals exhibit their behaviors in a natural way without explicit computation. Also, they show natural adaptive behaviors irrespective of unexpected external forces or changes of environment. This work is inspired from these novel features. Thus, a self-adapting robotic arm-hand control is proposed exploiting a control scheme based on central pattern generators (CPGs). Instead of a trajectory planning and inverse kinematics problem, this work endeavors to exploit robotic systems coupled with neural oscillators and virtual forces with joint velocity damping. We demonstrate self-adapting motions without the ill-posedness from extensive simulations that enable a robotic arm-hand to make adaptive changes from the given motion to a compliant motion. In addition, it is verified that reaching-to-grasping motion is possible by adopting only transit points sustaining motion repeatability under kinematic redundancy of joints.

In recent, control approaches for the human-like behavior in the field of service robotics have been attracting considerable attention, since most humans or animals perform various tasks uncomplicatedly. Hence simple control methods based on gaining a physical insight into human reaching movement in redundancy of DOFs have been proposed. In comparison with the conventional approaches, the proposed method tries to control directly robotic systems in task-space with the control signal composed of linear superposition of three terms 1) joint-damping, 2) virtual spring, and 3) virtual damper in task-space. In particular, our work contains a muscle tension effect of a human under the gravity. This give birth to energy efficient natural motions avoiding problems on repeatability of the motion and ill-posedness problems emerged in most of redundant DOF systems. Thus, this paper exhibits expendability of the position control into the orientation control and compliant behavior. It is verified with a real robotic arm that satisfies human-like movements and motion repeatability under kinematic redundancy of joints.

Recently, biologically inspired control approaches for robotic systems that involve the use of central pattern generators (CPGs) have been attracting considerable attention owing to the fact that most humans or animals move and walk easily without explicitly controlling their movements. Furthermore, they exhibit natural adaptive motions against unexpected disturbances or environmental changes without considering their kinematic configurations. Inspired by such novel phenomena, this paper endeavors to achieve self-adapting robotic arm motion. For this, biologically inspired CPG based control is proposed. In particular, this approach deals with crucial problems such as motion generation and repeatability of the joints emerged remarkably in most of redundant DOF systems. These problems can be overcome by employing a control based on artificial neural oscillators, virtual force and virtual muscle damping instead of trajectories planning and inverse kinematics. Biologically inspired motions can be attained if the joints of a robotic arm are coupled to neural oscillators and virtual muscles. We experimentally demonstrate self-adaptation motions that that enables a 7-DOF robotic arm to make adaptive changes from the given motion to a compliant motion. In addition, it is verified with real a real robotic arm that human-like movements and motion repeatability are satisfied under kinematic redundancy of joints.

This paper proposes a neural oscillator based control to attain rhythmically dynamic movements of a robot arm. In human or animal, it is known that neural oscillators could produce rhythmic commands efficiently and robustly under the changing task environment. In particular, entrainments of the neural oscillator play a key role to adapt the nervous system to the natural frequency of the interacted environments. Hence, we discuss how a robot arm controls for exhibiting natural adaptive motions as a controller employing the entrainment property. To demonstrate the excellence of entrainment, we implement the proposed control scheme to a real robot arm. Then this work shows the performance of the robot arm coupled to neural oscillators in various tasks that the arm traces a trajectory. Exploiting the neural oscillator and its entrainment property, we experimentally verify an impressive capability of self-adaptation of the neural oscillator that enables the robot arm to make adaptive changes corresponding to an exterior environment.

It is known that biologically inspired neural systems could exhibit natural dynamics efficiently and robustly for motion control, especially for rhythmic motion tasks. In addition, humans or animals exhibit natural adaptive motions without considering their kinematic configurations against unexpected disturbances or environment changes. In this paper, we focus on rhythmic arm motions that can be achieved by using a controller based on neural oscillators and virtual force. In comparison with conventional researches, this work treats neither trajectories planning nor inverse kinematics. Instead of those, a few desired points in task-space and a control method with Jacobian transpose and joint velocity damping are merely adopted. In addition, if the joints of robotic arms are coupled to neural oscillators, they may be capable of achieving biologically inspired motions corresponding to environmental changes. To verify the proposed control scheme, we perform some simulations to trace a desired motion and show the potential features related with self-adaptation that enables a three-link planar arm to make adaptive changes from the given motion to a compliant motion. Specifically, we investigate that human-like movements and motion repeatability are satisfied under kinematic redundancy of joints.

Humans or animals exhibit natural adaptive motions against unexpected disturbances or environment changes. In this paper, we focus on periodic, rhythmic arm motions that can be achieved by using a controller based on neural oscillators. The challenge of this work is to determine appropriate parameters of neural oscillators coupled to a robot arm, accomplishing a given task as well as self-sustaining natural rhythms. For this, an enhanced simulated annealing (SA) algorithm is developed. This work also demonstrates how to technically implement the proposed control scheme to a real robot. Exploiting the entrainment property of neural oscillators coupled to the joints of the arm, we verify that the arm traces a trajectory in such a way that the total energy consumption is minimized, responding to external disturbances.

Stable and robust dynamic locomotion has been gaining increasing attention in humanoid research. This paper presents a neural oscillator network for the generation of periodic locomotion patterns adapting to changes in the slope of the terrain. Specifically, locomotion trajectories of individual limbs are predetermined in the trajectory generator as a periodic function of the gait. The phase of the periodic function is coordinated with the output of the neural oscillator network incorporating sensory signals detecting the state of the foot in contact with the unknown changing terrain. For stability to be maintained, the neural oscillator plays an important role by controlling the trajectory of the COM in phase with the trajectory of the ZMP. In order to verify the validity of the proposed scheme, we carry out simulations and experiments. A preliminary investigation has yielded promising results, indicating that it may be applied to humanoid locomotion through uneven and uncertain terrain.

A parameter tuning scheme for the neural oscillator is addressed to achieve biologically inspired robot control architectures based on a neural oscillator. It would be desirable to determine appropriately unknown parameters of the neural oscillator to accomplish a task of rhythmic movement under various changes of environment. Human or animal exhibits natural dynamics with efficient and performs robust motions against unexpected disturbances or environment changes. The neural oscillator needs to be tuned using its optimal parameters to generate such natural movement. As simple examples, this paper connects the neural oscillator to a pendulum system and a rotating crank system. To determine the optimal parameters of the neural oscillator for the examples, the optimization scheme based on the Simulated Annealing (SA) method is used. We verify the performance of the given tasks with the obtained optimal parameters of the neural oscillator, showing the adaptation motions of the example systems with entrainment property in numerical simulations.

We propose a novel framework for imitation learning that helps a humanoid robot achieve its goal of learning. There are apparent discrepancies in shapes and sizes among humans and humanoid robots. It would be advantageous if robots could learn their behavior from different individuals. Toward this end, this paper discusses appropriate behavior generation method through imitation learning considering that demonstrator and imitator robots have different kinematics and dynamics. As part of a wider interest in behavior generation in general, this work mainly investigates how an imitator robot adapts a reference locomotion gait captured from a demonstrator robot. Specifically, a goal-directed adaptation process that we call self-adjusting adaptor is proposed to achieve stable locomotion of the imitator. The proposed adaptor has an important role that the perceived locomotion patterns are modified to keep the direction of lower leg contacting the ground identical between the demonstrator and the imitator, sustaining the dynamic stability by controlling the position of the center of mass. The validity of the proposed scheme is evaluated through simulations employing various imitator models on OpenHRP and then verified on a real robot