Design Optimization of Musculoskeletal Humanoids with Maximization of Redundancy to Compensate for Muscle Rupture

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.

Applications of Stretch Reflex for the Upper Limb of Musculoskeletal Humanoids: Protective Behavior, Postural Stability, and Active Induction

The musculoskeletal humanoid has various biomimetic benefits, and it is important that we can embed and evaluate human reflexes in the actual robot. Although stretch reflex has been implemented in lower limbs of musculoskeletal humanoids, we apply it to the upper limb to discover its useful applications. We consider the implementation of stretch reflex in the actual robot, its active/passive applications, and the change in behavior according to the difference of parameters.

Exceeding the Maximum Speed Limit of the Joint Angle for the Redundant Tendon-driven Structures of Musculoskeletal Humanoids

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.

Front Hair Styling Robot System Using Path Planning for Root-Centric Strand Adjustment

Hair styling is a crucial aspect of personal grooming, significantly influenced by the appearance of front hair. While brushing is commonly used both to detangle hair and for styling purposes, existing research primarily focuses on robotic systems for detangling hair, with limited exploration into robotic hair styling. This research presents a novel robotic system designed to automatically adjust front hairstyles, with an emphasis on path planning for root-centric strand adjustment. The system utilizes images to compare the current hair state with the desired target state through an orientation map of hair strands. By concentrating on the differences in hair orientation and specifically targeting adjustments at the root of each strand, the system performs detailed styling tasks. The path planning approach ensures effective alignment of the hairstyle with the target, and a closed-loop mechanism refines these adjustments to accurately evolve the hairstyle towards the desired outcome. Experimental results demonstrate that the proposed system achieves a high degree of similarity and consistency in front hair styling, showing promising results for automated, precise hairstyle adjustments.

Environmentally Adaptive Control Including Variance Minimization Using Stochastic Predictive Network with Parametric Bias: Application to Mobile Robots

In this study, we propose a predictive model composed of a recurrent neural network including parametric bias and stochastic elements, and an environmentally adaptive robot control method including variance minimization using the model. Robots which have flexible bodies or whose states can only be partially observed are difficult to modelize, and their predictive models often have stochastic behaviors. In addition, the physical state of the robot and the surrounding environment change sequentially, and so the predictive model can change online. Therefore, in this study, we construct a learning-based stochastic predictive model implemented in a neural network embedded with such information from the experience of the robot, and develop a control method for the robot to avoid unstable motion with large variance while adapting to the current environment. This method is verified through a mobile robot in simulation and to the actual robot Fetch.

Task-specific Self-body Controller Acquisition by Musculoskeletal Humanoids: Application to Pedal Control in Autonomous Driving

The musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex flexible body is difficult. Although we have developed an online acquisition method of the nonlinear relationship between joints and muscles, we could not completely match the actual robot and its self-body image. When realizing a certain task, the direct relationship between the control input and task state needs to be learned. So, we construct a neural network representing the time-series relationship between the control input and task state, and realize the intended task state by applying the network to a real-time control. In this research, we conduct accelerator pedal control experiments as one application, and verify the effectiveness of this study.

Design of a Five-Fingered Hand with Full-Fingered Tactile Sensors Using Conductive Filaments and Its Application to Bending after Insertion Motion

The purpose of this study is to construct a contact point estimation system for the both side of a finger, and to realize a motion of bending the finger after inserting the finger into a tool (hereinafter referred to as the bending after insertion motion). In order to know the contact points of the full finger including the joints, we propose to fabricate a nerve inclusion flexible epidermis by combining a flexible epidermis and a nerve line made of conductive filaments, and estimate the contact position from the change of resistance of the nerve line. A nerve inclusion flexible epidermis attached to a thin fingered robotic hand was combined with a twin-armed robot and tool use experiments were conducted. The contact information can be used for tool use, confirming the effectiveness of the proposed method.

Modification of muscle antagonistic relations and hand trajectory on the dynamic motion of Musculoskeletal Humanoid

In recent years, some research on musculoskeletal humanoids is in progress. However, there are some challenges such as unmeasurable transformation of body structure and muscle path, and difficulty in measuring own motion because of lack of joint angle sensor. In this study, we suggest two motion acquisition methods. One is a method to acquire antagonistic relations of muscles by tension sensing, and the other is a method to acquire correct hand trajectory by vision sensing. Finally, we realize badminton shuttlecock-hitting motion of Kengoro with these two acquisition methods.

Remote Life Support Robot Interface System for Global Task Planning and Local Action Expansion Using Foundation Models

Robot systems capable of executing tasks based on language instructions have been actively researched. It is challenging to convey uncertain information that can only be determined on-site with a single language instruction to the robot. In this study, we propose a system that includes ambiguous parts as template variables in language instructions to communicate the information to be collected and the options to be presented to the robot for predictable uncertain events. This study implements prompt generation for each robot action function based on template variables to collect information, and a feedback system for presenting and selecting options based on template variables for user-to-robot communication. The effectiveness of the proposed system was demonstrated through its application to real-life support tasks performed by the robot.

Vlimb: A Wire-Driven Wearable Robot for Bodily Extension, Balancing Powerfulness and Reachability

Numerous wearable robots have been developed to meet the demands of physical assistance and entertainment. These wearable robots range from body-enhancing types that assist human arms and legs to body-extending types that have extra arms. This study focuses specifically on wearable robots of the latter category, aimed at bodily extension. However, they have not yet achieved the level of powerfulness and reachability equivalent to that of human limbs, limiting their application to entertainment and manipulation tasks involving lightweight objects. Therefore, in this study, we develop an body-extending wearable robot, Vlimb, which has enough powerfulness to lift a human and can perform manipulation. Leveraging the advantages of tendon-driven mechanisms, Vlimb incorporates a wire routing mechanism capable of accommodating both delicate manipulations and robust lifting tasks. Moreover, by introducing a passive ring structure to overcome the limited reachability inherent in tendon-driven mechanisms, Vlimb achieves both the powerfulness and reachability comparable to that of humans. This paper outlines the design methodology of Vlimb, conducts preliminary manipulation and lifting tasks, and verifies its effectiveness.