Modular robots, which can form various configurations by connecting multiple modules, have the ability to perform diverse tasks by reconfiguring themselves. However, due to the torque limitations of a single module, lifting multiple modules simultaneously is challenging, making it difficult to construct manipulators for manipulation tasks. In this study, we propose TYCOON (modular robot capable of Torque sYnthesized COllabOratioN), a modular robot that can synthesize more torque by transmitting torque between modules. In conventional modular robots, each module functions as a joint when assembled (joint-drive function), enabling them to lift or rotate other modules. TYCOON modules have an additional torque-transmission function that enables modules to transmit their torque to adjacent modules, allowing the torques of multiple modules to be synthesized and increasing the driving torque per joint. To realize this, TYCOON employs a Complementary Intermittent Gear Mechanism consisting of two gears with complementary teeth, ensuring that when one gear is engaged the other remains disengaged, achieving two functionalities with a single drive mechanism. Evaluation experiments showed that while a single module could lift only two modules, TYCOON could lift up to four connected modules. This capability enabled a 6-DOF serial-link manipulator, demonstrating the effectiveness of torque transmission in modular robots.

In nature, birds perch to rest and to survey their predators and prey. In human-managed contexts, perching also facilitates interaction with humans such as falconry. Recently, researchers have developed perching-capable aerial robots as a way to save energy, and deformable structures 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 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 improves pressure regulation while integrating safe and compliant contact and adjustable grasping forces, enhancing interaction capabilities and reducing energy consumption. 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.

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.

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.

In robotics research that uses actuators to replace actual work in system integration, there is a growing demand for servo modules that we can use in various robots. We require the module to have the following three functions. 1) Supporting its weight without using energy, 2) the function to move the output link by external manipulation, and 3) being inexpensive and easy to duplicate and combine. In this study, we developed servo modules with openable worm gear reduction mechanisms ready for mass production to support a large load in the driven state and to move passively in the open state. As example configurations, we show 1: a winch for suspending a robot, 2: a non-backdrivable hand, 3: a teaching device, and 4: a carrying cart that can support its weight and carry heavy objects, demonstrating that the module can be easily used in a variety of configurations while fulfilling its intended functions.

Science Fiction Prototyping (SFP) is a method that uses science fiction to imagine future technologies and foster innovation. It is considered effective for exploring human-robot relationships and envisioning better robot designs. However, robot embodiment influences human perception, which plays a crucial role in interaction. Simply imagining future scenarios with robots through SFP may overlook these aspects. We propose an approach called Experiential Science Fiction Prototyping (ESFP), which adds a phase of experiencing the story to the traditional SFP process. To explore the effects of ESFP, we conducted a workshop with Japanese teenagers under the theme of designing a robot that contributes to a sense of “ibasho”—a Japanese concept referring to a space or relationship where one feels accepted and comfortable. ESFP unfolds in three phases: Storytelling, where participants envision future lives with robots and create stories; Experience, where they bring these stories to life through interaction with a physical robot; and Discussion, where they reflect on the story they created and experienced. The results suggested that, through the experiential phase, participants developed new ideas about interaction with robots and expanded their imagination about future relationships. Experiencing the story helped participants connect more closely with the envisioned robot interactions and inspired new reflections and expectations. This study contributes by proposing the ESFP method, detailing its implementation, and discussing its potential through a case study.

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.

Humans can autonomously learn the relationship between sensation and motion in their own bodies, estimate and control their own body states, and move while continuously adapting to the current environment. On the other hand, current robots control their bodies by learning the network structure described by humans from their experiences, making certain assumptions on the relationship between sensors and actuators. In addition, the network model does not adapt to changes in the robot's body, the tools that are grasped, or the environment, and there is no unified theory, not only for control but also for state estimation, anomaly detection, simulation, and so on. In this study, we propose a Generalized Multisensory Correlational Model (GeMuCo), in which the robot itself acquires a body schema describing the correlation between sensors and actuators from its own experience, including model structures such as network input/output. The robot adapts to the current environment by updating this body schema model online, estimates and controls its body state, and even performs anomaly detection and simulation. We demonstrate the effectiveness of this method by applying it to tool-use considering changes in grasping state for an axis-driven robot, to joint-muscle mapping learning for a musculoskeletal robot, and to full-body tool manipulation for a low-rigidity plastic-made humanoid.

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.

Various conditions exist in individual daily life environments. It is important for a daily life support robot to observe states in the daily life environment and perform tasks depending on the living environment. Today, pre-trained vision-language models have been developed and are good at the general interpretation of images. With these backgrounds, we propose a method to classify situations in real daily life environments for situation-aware task execution using the pre-trained vision-language model. Our classifier requires no additional training and is robust to minor pose changes of objects and robots. In our experiments, we have successfully clustered a variety of situations, ranging from object situations to human actions, and executed tasks based on the situation by mapping cluster results to tasks.

While general-purpose models for robot learning have been extensively studied in the field of navigation, their translational movement capabilities are often limited to quasi-static motion. In contrast, quadrotors have demonstrated agile obstacle avoidance through reinforcement learning (RL) in drone racing. However, such policies are specifically designed for agile quadrotors with particular dynamic models, limiting their applicability to other types of aerial vehicles. To address these two limitations, we present a general-purpose reinforcement learning framework that enables different velocity configurations for the quadrotor. The key point of our framework is the randomization of three key transitional parameters of the vehicle: 'Acc-Property' (maximum acceleration, response time of acceleration, and quadrotor radius for collision detection). We demonstrate that randomizing dynamics and controllers is necessary to generalize policies to different types of quadrotors in smooth flight. Furthermore, we evaluate our learned policy on existing quadrotor systems ranging in size from 0.5 meters to 1.0 meter on real hardware, achieving 100% static obstacle avoidance in 33 trials and 78.6% dynamic obstacle avoidance in 42 trials. To the best of our knowledge, this study represents the first exploration of a generalized policy for multirotor collision avoidance in non-static flight conditions.

Self‐healing is a promising approach for damage management in high‐load robot applications, such as legged robots. It is becoming a major function in soft robotics; however, its application to support heavyweight is relatively niche. Although previous studies has developed several self‐healing tensile modules for tendon‐driven robots, these modules suffered from deficient healing strength because of the formation of surface oxides. This study proposes a biomimetic approach to enhance self‐healing performance. This approach exploits the motion of the robot to trigger the sloshing of liquid metal, which decomposes surface oxide. The method is validated using a benchtop module test, resulting in a healed strength of over tens of kilograms. Moreover, the module enables the tendon‐driven monopod testbed to perform a squat motion 13 times after a landing impact fracture and self‐healing sequence. The self‐healing module does not break during or after the squatting motion. To the best of our knowledge, this is the first demonstration of active self‐healing behavior using a life‐sized legged robot. Thus, this study provides a novel approach in the field of self‐healing robotics for improving self‐healing, thus contributing to medical robot and mechatronic designs, including rehabilitation, surgical, and diagnostic robots.

Robots generally have a structure that combines rotational joints and links in a serial fashion. On the other hand, various joint mechanisms are being utilized in practice, such as prismatic joints, closed links, and wire-driven systems. Previous research have focused on individual mechanisms, proposing methods to design robots capable of achieving given tasks by optimizing the length of links and the arrangement of the joints. In this study, we propose a method for the design optimization of robots that combine different types of joints, specifically rotational and prismatic joints. The objective is to automatically generate a robot that minimizes the number of joints and link lengths while accomplishing a desired task, by utilizing a black-box multi-objective optimization approach. This enables the simultaneous observation of a diverse range of body designs through the obtained Pareto solutions. Our findings confirm the emergence of practical and known combinations of rotational and prismatic joints, as well as the discovery of novel joint combinations.

Musculoskeletal humanoids possess flexible and redundant bodies that closely resemble the human body. However, their application in real-world tasks has been impeded by the challenges involved in achieving bipedal walking with flexible bodies. To address this issue, we developed Musashi-W, a musculoskeletal wheeled robot. The Musashi-W comprises an upper body musculoskeletal system, lower body wheels, a linear motion mechanism that corresponds to the back, and a rotatable head equipped with a camera. Nevertheless, managing the combination of different circuit systems and drive systems has been a challenge. Hence, we developed an integrated infrastructure capable of managing these systems seamlessly. Moreover, body schema learning, reflex control, and the utilization of variable stiffness are crucial for handling musculoskeletal humanoids. We have combined these elements to construct a task realization system using musculoskeletal humanoids. We demonstrated the effectiveness of our system by realizing a table-setting task through dynamic cloth manipulation using variable stiffness.