Compliance in robot actuation provides a solution to perform safe physical human-robot interaction. Conventional compliant actuators (variable stiffness actuators, series elastic actuators) used more than two motors or closed-loop controller to modulate both stiffness and equilibrium position independently. These actuators are complex, lack of energy efficiency, and have limited stiffness range. In conjunction with an active, positive stiffness modulation, implementing a passive negative stiffness element enabled a compact design of the compliant actuator. This paper suggests a power transmission design of fast and energy-efficient stiffness modulation based on this new compliant actuator concept. First, the double slider-crank mechanism made fast stiffness modulation and high energy-efficiency. Second, positioning the leaf spring’s bending location to the center also enabled the fast stiffness modulation speed and wide range stiffness modulation. Third, optimized elliptical cam with compression spring generated negative stiffness in output. We provide theoretical modeling of each mechanical drivetrains and characterization of positive stiffness modulation (range and speed) and negative stiffness with corresponding power consumption experimentally.

High mechanical impedance, sensor resolution, and computing bandwidth are desirable for achieving stability in feedback control. In contrast, although the human body is physically inferior to man-made systems in terms of feedback control due to signal transmission delay, humans can achieve not only stable but also robust and adaptive control ability. For these reasons, the significance of the feedforward control of the human has been emphasized in neuroscience. In previous studies, virtual trajectory control and internal model hypotheses were used to explain the principle of human feedforward control in view of the mechanical stiffness of joints and the internal model of the brain, respectively. Inspired by these insights, in this paper, we focus on the relationship between the joint stiffness and inverse model accuracy and attempted to apply it to the field of robotics. We present a variable stiffness actuator developed using variable radius gear transmission inspired by muscle cross-bridge kinematics. The developed mechanism allows the joint stiffness to be accurately controlled without any sensor-based feedback control. Using the developed actuator, we conduct a feedforward motion generating experiment with respect to variations in the inverse dynamics model uncertainty and verified that joint stiffness can compensate for inverse model uncertainty and external disturbances. These results indicate that the developed variable stiffness actuator can be applied to the robotics field for feedforward applications and support the hypothesis that humans may utilize joint stiffness to compensate for the inverse dynamics model uncertainty.

Inspired from Hogan's myoelectric processor in 1980, this study presents a stochastic sEMG processing method to estimate the muscle activation level for manipulator control. Hogan's previous study showed the feasibility to estimate the muscle activation level with multi-channel sEMG under static force condition. However, it is difficult to continuously estimate muscle activation during dynamic contraction because of the nonlinear effects by the time-varying nature of sEMG. To enhance the performance of high SNR and rapid response in force-varying contraction, we propose a new method with statistical analysis extended from a whitening method of Hogan's study. The signals from eight sEMG channels were used to estimate the muscle activation level during isometric force-varying contractions. Experimentally, a two-DoF manipulator was controlled by input signals from the estimated muscle activation signal.

The closer distance between robots and human partners in the same space makes compliant robots essential in these days. While torque sensor based impedance control scheme suffers from insufficient bandwidth, musculoskeletal systems can achieve similar ability with smaller resources by using mechanically guaranteed dynamics. In this study, inspired by active stiffness mechanism of biological muscle, we developed a variable stiffness actuator which can realize precise joint stiffness without torque sensor based feedback control. The actuator consists of two antagonistically allocated DC motors with a variable radius gear transmission mechanism. The equilibrium position and joint stiffness can be independently controlled adjusting the activation level of motors by feedforward control. Theoretically predicted stiffness is realized well. In this paper, the design, functional principle and control of the actuator are introduced with analytical investigation.

The stabilization of man-made dynamic systems has been achieved by sensor-based state feedback control with high computational bandwidth, fast signal transmission speed, and stiff joints. In contrast, many biological systems can achieve similar or superior stable behavior with low computational bandwidth, slow signal transmission speed via the nervous system, and flexible joints. The concept of self-stabilization has recently been proposed and widely investigated to explain this phenomenon. Self-stabilization is defined as the ability to restore its original state after a disturbance without any feedback control. In this paper, the stabilizing function of a musculoskeletal system for arbitrary motion in the vertical plane is analytically investigated using Lyapunov stability criteria. Based on this investigation, the method of designing a new actuator that can assign a self-stabilizing function to a robotic arm is introduced and a self-stabilizing manipulator is physically realized. As a result, a theoretically predicted self-stabilizing function is experimentally verified and explains why a biological musculoskeletal system can be stabilized with feedforward control.

The stabilization of man-made artificial systems has been achieved by sensor based state feedback control with high computational bandwidth and high stiffness structures. In contrast, many biological systems have been achieved similar or superior stable behavior with low speed signal transmission via nervous systems, which is easy to introduce unstable performance from a control engineering perspective. In order to explain this phenomenon, the concept of self-stabilization has recently been proposed and investigated. Self-stabilization is defined as the ability to restore its original state after a disturbance without any feedback control. In this paper, the self-stabilizing function of a musculoskeletal system for arbitrary motion in the vertical plane is analytically investigated using Lyapunov stability theory. Based on this investigation we propose a design method to realize the self-stabilizing function of a musculoskeletal system, and experimentally verify that the self-stabilizing function can be physically realized by the proposed Lyapunov function.