A robot designed to coexist and work with humans in the same workspace should be able to work at the same speed as humans and have safe contact with humans and with the environment. However, when a robot arm has been given flexibility through mechanisms and controls for the purpose of coexistence, it is difficult for it to perform tasks at the speed and accuracy desired by humans if it is moved simply by using conventional position-based controls. With such an arm, we consider that the use of learning-based control is necessary to achieve both safety and speed. Therefore, we prototyped a low-inertia, high-backdrivability arm as a platform for studying learning-based control and tested two types of learning-based control. This paper describes our design process, in which hardware suitable for learning-based control was developed according to the requirements of the specific task. It also presents the results of our evaluation experiments, in which tasks involving quick movements and motion requiring physical contact with an object were performed using learning-based control.

Self-healing function is a promising approach for damage management of high-load robot applications such as legged robots. Although the function is getting major in soft robotics, its application to life-sized "stiff" robots is of relatively minor interest. Although the authors have devised several self-healing tensile modules for tendon-driven robots, the design guideline to satisfy the large load endurance and large stroke is still unclear. The paper focuses on the parametric design for unleaked liquid-assisted healing of low melting point alloy structure. The method was validated with a benchtop module test. Moreover, the module enabled tendon-driven monopod testbed to perform squat motion three times after the landing impact fracture and the self-healing sequence, which was never accomplished.

We have implemented a force-based operational space controller on a physical musculoskeletal humanoid robot arm. The controller calculates muscle activations based on a biomimetic Hill-type muscle model. We propose a method to include the joint torque nullspace in the optimization process, which enables the robot to exploit the nullspace to gradually lower its overall muscle activation. We have verified in experiments that it can react compliantly to external disturbances while retaining its operational space task.

Musculoskeletal humanoids have various biomimetic advantages, and the redundant muscle arrangement allowing for variable stiffness control is one of the most important. In this study, we focus on one feature of the redundancy, which enables the humanoid to keep moving even if one of its muscles breaks, an advantage that has not been dealt with in many studies. In order to make the most of this advantage, the design of muscle arrangement is optimized by considering the maximization of minimum available torque that can be exerted when one muscle breaks. This method is applied to the elbow of a musculoskeletal humanoid Musashi with simulations, the design policy is extracted from the optimization results, and its effectiveness is confirmed with the actual robot.

In this research, we have taken a biomimetic approach to the control of musculoskeletal humanoids. A controller was designed based on the motor directional tuning phenomenon seen in the motor cortex of primates. Despite the simple implementation of the control scheme, complex coordinated movements such as reaching for target objects with its upper body was achieved, and is demonstrated in the accompanying video. The controller does not require an internal model, and instead constantly observes its body in relation to the external world to update motor commands. We claim that such an embodied approach to the control of musculoskeletal robots will be able to effectively take advantage of their complex bodies to achieve motion.