The musculoskeletal humanoid has various biomimetic benefits, and the redundant muscle arrangement is one of its most important characteristics. This redundancy can achieve fail-safe redundant actuation and variable stiffness control. However, there is a problem that the maximum joint angle velocity is limited by the slowest muscle among the redundant muscles. In this study, we propose two methods that can exceed the limited maximum joint angle velocity, and verify the effectiveness with actual robot experiments.

As robotics continue to enter various sectors beyond traditional industrial applications, the need for intuitive robot training and interaction systems becomes increasingly more important. This paper introduces Robotic Augmented Reality for Machine Programming (RAMPA), a system that utilizes the capabilities of state-of-the-art and commercially available AR headsets, e.g., Meta Quest 3, to facilitate the application of Programming from Demonstration (PfD) approaches on industrial robotic arms, such as Universal Robots UR10. Our approach enables in-situ data recording, visualization, and fine-tuning of skill demonstrations directly within the user's physical environment. RAMPA addresses critical challenges of PfD, such as safety concerns, programming barriers, and the inefficiency of collecting demonstrations on the actual hardware. The performance of our system is evaluated against the traditional method of kinesthetic control in teaching three different robotic manipulation tasks and analyzed with quantitative metrics, measuring task performance and completion time, trajectory smoothness, system usability, user experience, and task load using standardized surveys. Our findings indicate a substantial advancement in how robotic tasks are taught and refined, promising improvements in operational safety, efficiency, and user engagement in robotic programming.

Tendon-driven musculoskeletal humanoids typically have complex structures similar to those of human beings, such as ball joints and the scapula, in which encoders cannot be installed. Therefore, joint angles cannot be directly obtained and need to be estimated using the changes in muscle lengths. In previous studies, methods using table-search and extended kalman filter have been developed. These methods express the joint-muscle mapping, which is the nonlinear relationship between joint angles and muscle lengths, by using a data table, polynomials, or a neural network. However, due to computational complexity, these methods cannot consider the effects of polyarticular muscles. In this study, considering the limitation of the computational cost, we reduce unnecessary degrees of freedom, divide joints and muscles into several groups, and formulate a joint angle estimation method that takes into account polyarticular muscles. Also, we extend the estimation method to propose a joint angle estimation method using only the relative changes in muscle lengths. By this extension, which does not use absolute muscle lengths, we do not need to execute a difficult calibration of muscle lengths for tendon-driven musculoskeletal humanoids. Finally, we conduct experiments in simulation and actual environments, and verify the effectiveness of this study.

The body structures of tendon-driven musculoskeletal humanoids are complex, and accurate modeling is difficult, because they are made by imitating the body structures of human beings. For this reason, we have not been able to move them accurately like ordinary humanoids driven by actuators in each axis, and large internal muscle tension and slack of tendon wires have emerged by the model error between its geometric model and the actual robot. Therefore, we construct a joint-muscle mapping (JMM) using a neural network (NN), which expresses a nonlinear relationship between joint angles and muscle lengths, and aim to move tendon-driven musculoskeletal humanoids accurately by updating the JMM online from data of the actual robot. In this study, the JMM is updated online by using the vision of the robot so that it moves to the correct position (Vision Updater). Also, we execute another update to modify muscle antagonisms correctly (Antagonism Updater). By using these two updaters, the error between the target and actual joint angles decrease to about 40% in 5 minutes, and we show through a manipulation experiment that the tendon-driven musculoskeletal humanoid Kengoro becomes able to move as intended. This novel system can adapt to the state change and growth of robots, because it updates the JMM online successively.

Abstract-- In this study, a chair-type asymmetric tripedal low-rigidity robot was designed based on the three-legged chair character in the movie "Suzume" and its gait was generated. Its body structure consists of three legs that are asymmetric to the body, so it cannot be easily balanced. In addition, the actuator is a servo motor that can only feed-forward rotational angle commands and the sensor can only sense the robot's posture quaternion. In such an asymmetric and imperfect body structure, we analyzed how gait is generated in walking and stand-up motions by generating gaits with two different methods: a method using linear completion to connect the postures necessary for the gait discovered through trial and error using the actual robot, and a method using the gait generated by reinforcement learning in the simulator and reflecting it to the actual robot. Both methods were able to generate gait that realized walking and stand-up motions, and interesting gait patterns were observed, which differed depending on the method, and were confirmed on the actual robot.

Tendon-driven musculoskeletal humanoids have many benefits in terms of the flexible spine, multiple degrees of freedom, and variable stiffness. At the same time, because of its body complexity, there are problems in controllability. First, due to the large difference between the actual robot and its geometric model, it cannot move as intended and large internal muscle tension may emerge. Second, movements which do not appear as changes in muscle lengths may emerge, because of the muscle route changes caused by softness of body tissue. To solve these problems, we construct two models: ideal joint-muscle model and muscle-route change model, using a neural network. We initialize these models by a man-made geometric model and update them online using the sensor information of the actual robot. We validate that the tendon-driven musculoskeletal humanoid Kengoro is able to obtain a correct self-body image through several experiments.

We have developed a parallel wire-driven monopedal robot, RAMIEL, which has both speed and power due to the parallel wire mechanism and a long acceleration distance. RAMIEL is capable of jumping high and continuously, and so has high performance in traveling. On the other hand, one of the drawbacks of a minimal parallel wire-driven robot without joint encoders is that the current joint velocities estimated from the wire lengths oscillate due to the elongation of the wires, making the values unreliable. Therefore, despite its high performance, the control of the robot is unstable, and in 10 out of 16 jumps, the robot could only jump up to two times continuously. In this study, we propose a method to realize a continuous jumping motion by reinforcement learning in simulation, and its application to the actual robot. Because the joint velocities oscillate with the elongation of the wires, they are not used directly, but instead are inferred from the time series of joint angles. At the same time, noise that imitates the vibration caused by the elongation of the wires is added for transfer to the actual robot. The results show that the system can be applied to the actual robot RAMIEL as well as to the stable continuous jumping motion in simulation.

Sim-to-real is a mainstream method to cope with the large number of trials needed by typical deep reinforcement learning methods. However, transferring a policy trained in simulation to actual hardware remains an open challenge due to the reality gap. In particular, the characteristics of actuators in legged robots have a considerable influence on sim-to-real transfer. There are two challenges: 1) High reduction ratio gears are widely used in actuators, and the reality gap issue becomes especially pronounced when backdrivability is considered in controlling joints compliantly. 2) The difficulty in achieving stable bipedal locomotion causes typical system identification methods to fail to sufficiently transfer the policy. For these two challenges, we propose 1) a new simulation model of gears and 2) a method for system identification that can utilize failed attempts. The method's effectiveness is verified using a biped robot, the ROBOTIS-OP3, and the sim-to-real transferred policy can stabilize the robot under severe disturbances and walk on uneven surfaces without using force and torque sensors.

In this paper, we propose a method for online estimation of the robot's posture. Our method uses von Mises and Bingham distributions as probability distributions of joint angles and 3D orientation, which are used in directional statistics. We constructed a particle filter using these distributions and configured a system to estimate the robot's posture from various sensor information (e.g., joint encoders, IMU sensors, and cameras). Furthermore, unlike tangent space approximations, these distributions can handle global features and represent sensor characteristics as observation noises. As an application, we show that the yaw drift of a 6-axis IMU sensor can be represented probabilistically to prevent adverse effects on attitude estimation. For the estimation, we used an approximate model that assumes the actual robot posture can be reproduced by correcting the joint angles of a rigid body model. In the experiment part, we tested the estimator's effectiveness by examining that the joint angles generated with the approximate model can be estimated using the link pose of the same model. We then applied the estimator to the actual robot and confirmed that the gripper position could be estimated, thereby verifying the validity of the approximate model in our situation.

The use of cables for aerial manipulation has shown to be a lightweight and versatile way to interact with objects. However, fastening objects using cables is still a challenge and human is required. In this work, we propose a novel way to secure objects using hitches. The hitch can be formed and morphed in midair using a team of aerial robots with cables. The hitch's shape is modeled as a convex polygon, making it versatile and adaptable to a wide variety of objects. We propose an algorithm to form the hitch systematically. The steps can run in parallel, allowing hitches with a large number of robots to be formed in constant time. We develop a set of actions that include different actions to change the shape of the hitch. We demonstrate our methods using a team of aerial robots via simulation and actual experiments.