This paper presents an overview of AiDIN-VI, a force-controllable quadruped robot system with incorporated mandatory abilities of speed, efficiency, and mobility for providing real-world services. This paper describes the design methodologies and principles employed to implement these requisite capabilities on a single robot platform; particularly, the torque sensing method, along with the components and modularization method of the torque-controllable actuator unit are elucidated herein. The developed robot platform is equipped with all necessary components, including onboard PCs (for motion generation and vision mapping), a battery, modular actuator units, a wireless network router, and a remote e-stop controller for autonomous and manual locomotion control. The robot is subdivided into joints, legs, and robots, and its performance is experimentally tested. The capability of the robot with regard to joint torque control (± 70 Nm), leg force control (350 N along the z-axis), robot (vertical loading 25 kg, pulling force 200 N) were experimentally verified, and locomotive performances (walk, pace, trot, and jump) on various terrains were executed. A maximum trot gait speed of 1.2 m/s was recorded, along with minimum costs of transport of 1.187 and 1.15 at a speed of 1.0 m/s and under loading conditions, respectively.

Twisted-coiled polymer actuators (TCA) have many interesting properties that show potentials for making high performance bionic devices. This letter presents a long short term memory (LSTM) model to predict the behavior of an antagonistic joint driven by hybrid TCA bundles made from Spandex and nylon fibers. By using automatic differentiation, which can be done with PyTorch, a linearized discrete state space model can be formulated and then be utilized in model predictive control (MPC). The experimental results show that by combining LSTM and MPC in modeling and control of the TCA, both high prediction performance and control performance can be achieved. It is verified that using the LSTM model, the joint angle and actuator temperatures can be predicted accurately with an avarage steady state error less than 0.1 deg and 0.2 Cel. deg., respectively. The MPC control results show the controller's ability to reach the desired joint angle with an avarage steady state error of 0.21 degrees in set-point regulation and track a sinusoidal waveform at composite frequencies of 0.1 Hz to 0.15 Hz with the steady-state error less than 0.38 degrees while changing joint stiffness.

In a physical human-robot interaction (pHRi) system, improving transparency which allows humans to move as if there is no robot is a challenging topic. In general physical human-robot interaction, usage of the multiaxial force sensor for robot control is the norm. However the signal measured from the force sensor contains not only the force applied to the robot by the human motion intention but also the influence of the natural force feedback between the robot and the human hand produced by the robot motion. Therefore, in order to improve the transparency, it is necessary to charcterize the dynamics of the human hand as well as the dynamics of the robot. In this paper, an algorithm to improve the transparency is proposed. The proposed algorithm uses only a multiaxial force sensor and compensates the natural force feedback by using the human hand impedance. And it further improves the transparency of the pHRi system which has a limitation adjusting admittance parameters. In order to verify the algorithm, a device that can measure the impedance of the human hand is introduced, and a system model analysis and experiments using the hydraulic upper limb exoskeleton are carried out.

This letter proposes a variable stiffness robotic hand driven by twisted-coiled polymer actuators (TCAs) connected in series with a servomotor. Unlike slow conventional TCA systems, the grasping motion of the proposed hand is realized by the servomotor, which improves the speed of actuation significantly. It was shown that by using the servomotor, the rise time and settling time in set-point regulation control were $\text{0.1}\;{\mathrm {s}}$ and $\text{0.33}\;{\mathrm {s}}$ , respectively, which were significantly improved compared with those results of 3.26 s and 12.40 s using TCAs only. Since the stiffness of TCAs can be changed by varying their temperature, the stiffness of the hand can be realized by changing the temperature of the TCA. By applying model predictive control (MPC) and active cooling, the temperature of the TCA can be controlled to follow a sinusoidal waveform at a frequency of 0.1 Hz with an average steady-state error of 0.9 Celsius degrees. The proposed robotic hand can grasp a load of 80 g and approximately achieve a 5-fold variable stiffness. In addition, the fingers of the hand with soft joints are fully made using flexible thermoplastic polyurethane (TPU), therefore reducing the need for complex assembly processes.

For soft grippers to be applied in atypical industrial environments, they must conform to an object’s exterior shape and momentarily change their stiffness. However, many of the existing grippers have limitations with respect to these functions: they grasp an object with only a single curvature and a fixed stiffness. Consequently, those constraints limit the stability of grasping and the applications. This paper introduces a new multicurvature, variable-stiffness soft gripper. Inspired by the human phalanx and combining the phalanx structure and particle jamming, this work guarantees the required grasping functions. Unlike the existing soft pneumatic grippers with one curvature and one stiffness, this work tries to divide the pressurized actuating region into three parts to generate multiple curvatures for a gripper finger, enabling the gripper to increase its degrees of freedom. Furthermore, to prevent stiffness loss at an unpressurized segment, this work combines divided actuation and the variable-stiffness capability, which guarantee successful grasping actions. In summary, this gripper generates multiple grasping curvatures with the proper stiffness, enhancing its dexterity. This work introduces the new soft gripper’s design, analytical modeling, and fabrication method and verifies the analytic model by comparing it with FEM simulations and experimental results.