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Masayuki Inaba

Publications and source records attributed to Masayuki Inaba.

At least 19 recordsLinked to original sources

Design, Control, and Motion Strategy for DELTA: Transformable Multilink Multirotor for Air-Ground Hybrid Locomotion and Manipulation

In recent years, multimodal locomotion capabilities have enabled robots to maneuver in both terrestrial and aerial domains. However, most of these robots are designed only for locomotion, and few possess the manipulation capabilities required for practical tasks. By adding a manipulator, ground robots can perform manipulation, and some drones with robotic arms have demonstrated aerial manipulation. Nonetheless, such multirotors cannot be directly used for manipulation on the ground, and this configuration itself is unsuitable for air-ground hybrid locomotion. This is because their thruster-centralized structure makes it difficult to achieve both sufficient degrees of freedom (DoF) for manipulation and stable motion with contact and transformation. Therefore, in this work, we develop a new multilink multirotor with thrusters on each link and capable of contact with the environments. This robot can perform terrestrial rolling locomotion, aerial flight locomotion, and manipulation in multiple environments using joint actuation. First, we introduce a minimal configuration design of the proposed robot. We also describe a kinematic model and propose a design for each component based on this model. Second, we propose a real-time control method based on nonlinear optimization that considers contact and joint motion, which can be applied to various multirotors. Third, we propose motion strategies that include contact constraints specific to air-ground hybrid multilink multirotors, and analyze the limitations of manipulation capabilities based on multi-contact model. Finally, we demonstrate a variety of motions in both domains using the implemented prototype. To the best of our knowledge, this is the first demonstration of air-ground hybrid locomotion and manipulation by a multilink multirotor.

cs.RO

Characteristics, Management, and Utilization of Muscles in Musculoskeletal Humanoids: Empirical Study on Kengoro and Musashi

Various musculoskeletal humanoids have been developed so far, and numerous studies on control mechanisms have been conducted to leverage the advantages of their biomimetic bodies. However, there has not been sufficient and unified discussion on the diverse properties inherent in these musculoskeletal structures, nor on how to manage and utilize them. Therefore, this study categorizes and analyzes the characteristics of muscles, as well as their management and utilization methods, based on the various research conducted on the musculoskeletal humanoids we have developed, Kengoro and Musashi. We classify the features of the musculoskeletal structure into five properties: Redundancy, Independency, Anisotropy, Variable Moment Arm, and Nonlinear Elasticity. We then organize the diverse advantages and disadvantages of musculoskeletal humanoids that arise from the combination of these properties. In particular, we discuss body schema learning and reflex control, along with muscle grouping and body schema adaptation. Also, we describe the implementation of movements through an integrated system and discuss future challenges and prospects.

cs.RO

Flexible Morphing Aerial Robot with Inflatable Structure for Perching-based Human-Robot Interaction

Birds in nature perform perching not only for rest but also for interaction with human such as the relationship with falconers. Recently, researchers achieve perching-capable aerial robots as a way to save energy, and deformable structure demonstrate significant advantages in efficiency of perching and compactness of configuration. However, ensuring flight stability remains challenging for deformable aerial robots due to the difficulty of controlling flexible arms. Furthermore, perching for human interaction requires high compliance along with safety. Thus, this study aims to develop a deformable aerial robot capable of perching on humans with high flexibility and grasping ability. To overcome the challenges of stability of both flight and perching, we propose a hybrid morphing structure that combines a unilateral flexible arm and a pneumatic inflatable actuators. This design allows the robot's arms to remain rigid during flight and soft while perching for more effective grasping. We also develop a pneumatic control system that optimizes pressure regulation while integrating shock absorption and adjustable grasping forces, enhancing interaction capabilities and energy efficiency. Besides, we focus on the structural characteristics of the unilateral flexible arm and identify sufficient conditions under which standard quadrotor modeling and control remain effective in terms of flight stability. Finally, the developed prototype demonstrates the feasibility of compliant perching maneuvers on humans, as well as the robust recovery even after arm deformation caused by thrust reductions during flight. To the best of our knowledge, this work is the first to achieve an aerial robot capable of perching on humans for interaction.

cs.RO

DIJE: Dense Image Jacobian Estimation for Robust Robotic Self-Recognition and Visual Servoing

For robots to move in the real world, they must first correctly understand the state of its own body and the tools that it holds. In this research, we propose DIJE, an algorithm to estimate the image Jacobian for every pixel. It is based on an optical flow calculation and a simplified Kalman Filter that can be efficiently run on the whole image in real time. It does not rely on markers nor knowledge of the robotic structure. We use the DIJE in a self-recognition process which can robustly distinguish between movement by the robot and by external entities, even when the motion overlaps. We also propose a visual servoing controller based on DIJE, which can learn to control the robot's body to conduct reaching movements or bimanual tool-tip control. The proposed algorithms were implemented on a physical musculoskeletal robot and its performance was verified. We believe that such global estimation of the visuomotor policy has the potential to be extended into a more general framework for manipulation.

cs.RO

Optimization-based Posture Generation for Whole-body Contact Motion by Contact Point Search on the Body Surface

Whole-body contact is an effective strategy for improving the stability and efficiency of the motion of robots. For robots to automatically perform such motions, we propose a posture generation method that employs all available surfaces of the robot links. By representing the contact point on the body surface by two-dimensional configuration variables, the joint positions and contact points are simultaneously determined through a gradient-based optimization. By generating motions with the proposed method, we present experiments in which robots manipulate objects effectively utilizing whole-body contact.

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Do Developers Depend on Deprecated Library Versions? A Mining Study of Log4j

Log4j has become a widely adopted logging library for Java programs due to its long history and high reliability. Its widespread use is notable not only because of its maturity but also due to the complexity and depth of its features, which have made it an essential tool for many developers. However, Log4j 1.x, which reached its end of support (deprecated), poses significant security risks and has numerous deprecated features that can be exploited by attackers. Despite this, some clients may still rely on this library. We aim to understand whether clients are still using Log4j 1.x despite its official support ending. We utilized the Mining Software Repositories 2025 challenge dataset, which provides a large and representative sample of open-source software projects. We analyzed over 10,000 log entries from the Mining Software Repositories 2025 challenge dataset using the Goblin framework to identify trends in usage rates for both Log4j 1.x and Log4j-core 2.x. Specifically, our study addressed two key issues: (1) We examined the usage rates and trends for these two libraries, highlighting any notable differences or patterns in their adoption. (2) We demonstrate that projects initiated after a deprecated library has reached the end of its support lifecycle can still maintain significant popularity. These findings highlight how deprecated are still popular, with the next step being to understand the reasoning behind these adoptions.

cs.SE

Development of a Five-Fingerd Biomimetic Soft Robotic Hand by 3D Printing the Skin and Skeleton as One Unit

Robot hands that imitate the shape of the human body have been actively studied, and various materials and mechanisms have been proposed to imitate the human body. Although the use of soft materials is advantageous in that it can imitate the characteristics of the human body's epidermis, it increases the number of parts and makes assembly difficult in order to perform complex movements. In this study, we propose a skin-skeleton integrated robot hand that has 15 degrees of freedom and consists of four parts. The developed robotic hand is mostly composed of a single flexible part produced by a 3D printer, and while it can be easily assembled, it can perform adduction, flexion, and opposition of the thumb, as well as flexion of four fingers.

cs.RO

Reflex-based Motion Strategy of Musculoskeletal Humanoids under Environmental Contact Using Muscle Relaxation Control

The musculoskeletal humanoid can move well under environmental contact thanks to its body softness. However, there are few studies that actively make use of the environment to rest its flexible musculoskeletal body. Also, its complex musculoskeletal structure is difficult to modelize and high internal muscle tension sometimes occurs. To solve these problems, we develop a muscle relaxation control which can minimize the muscle tension by actively using the environment and inhibit useless internal muscle tension. We apply this control to some basic movements, the motion of resting the arms on the desk, and handle operation, and verify its effectiveness.

cs.RO

Online Learning of Danger Avoidance for Complex Structures of Musculoskeletal Humanoids and Its Applications

The complex structure of musculoskeletal humanoids makes it difficult to model them, and the inter-body interference and high internal muscle force are unavoidable. Although various safety mechanisms have been developed to solve this problem, it is important not only to deal with the dangers when they occur but also to prevent them from happening. In this study, we propose a method to learn a network outputting danger probability corresponding to the muscle length online so that the robot can gradually prevent dangers from occurring. Applications of this network for control are also described. The method is applied to the musculoskeletal humanoid, Musashi, and its effectiveness is verified.

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Stability Recognition with Active Vibration for Bracing Behaviors and Motion Extensions Using Environment in Musculoskeletal Humanoids

Although robots with flexible bodies are superior in terms of the contact and adaptability, it is difficult to control them precisely. On the other hand, human beings make use of the surrounding environments to stabilize their bodies and control their movements. In this study, we propose a method for the bracing motion and extension of the range of motion using the environment for the musculoskeletal humanoid. Here, it is necessary to recognize the stability of the body when contacting the environment, and we develop a method to measure it by using the change in sensor values of the body when actively vibrating a part of the body. Experiments are conducted using the musculoskeletal humanoid Musashi, and the effectiveness of this method is confirmed.

cs.RO

CoverLib: Classifiers-equipped Experience Library by Iterative Problem Distribution Coverage Maximization for Domain-tuned Motion Planning

Library-based methods are known to be very effective for fast motion planning by adapting an experience retrieved from a precomputed library. This article presents CoverLib, a principled approach for constructing and utilizing such a library. CoverLib iteratively adds an experience-classifier-pair to the library, where each classifier corresponds to an adaptable region of the experience within the problem space. This iterative process is an active procedure, as it selects the next experience based on its ability to effectively cover the uncovered region. During the query phase, these classifiers are utilized to select an experience that is expected to be adaptable for a given problem. Experimental results demonstrate that CoverLib effectively mitigates the trade-off between plannability and speed observed in global (e.g. sampling-based) and local (e.g. optimization-based) methods. As a result, it achieves both fast planning and high success rates over the problem domain. Moreover, due to its adaptation-algorithm-agnostic nature, CoverLib seamlessly integrates with various adaptation methods, including nonlinear programming-based and sampling-based algorithms.

cs.RO

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.

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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.

cs.RO

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.

cs.RO

Behavioral Learning of Dish Rinsing and Scrubbing based on Interruptive Direct Teaching Considering Assistance Rate

Robots are expected to manipulate objects in a safe and dexterous way. For example, washing dishes is a dexterous operation that involves scrubbing the dishes with a sponge and rinsing them with water. It is necessary to learn it safely without splashing water and without dropping the dishes. In this study, we propose a safe and dexterous manipulation system. The robot learns a dynamics model of the object by estimating the state of the object and the robot itself, the control input, and the amount of human assistance required (assistance rate) after the human corrects the initial trajectory of the robot's hands by interruptive direct teaching. By backpropagating the error between the estimated and the reference value using the acquired dynamics model, the robot can generate a control input that approaches the reference value, for example, so that human assistance is not required and the dish does not move excessively. This allows for adaptive rinsing and scrubbing of dishes with unknown shapes and properties. As a result, it is possible to generate safe actions that require less human assistance.

cs.RO

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.

cs.RO

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.

cs.RO

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.

cs.RO