Reliable object localization is a critical challenge in drone-based aerial manipulation, particularly when objects are outside the camera's field of view. This paper presents a new approach to enhance drone reliability in aerial grasping tasks by integrating a 1D time-of-flight range sensor with a vision-based localization system. The range sensor, positioned beneath the drone, generates a detailed point cloud of the ground beneath the drone, allowing for precise object localization even when the drone hovers directly above the target. By combining visual tracking with realtime distance measurements, our system achieves a 96 % grasp success rate across 128 trials with diverse objects, representing a significant improvement over previous approaches. This method enables zero-shot grasping without prior knowledge of the objects, increasing versatility and robustness in complex, unstructured environments. The open-source software and hardware design of the platform provide a foundation for further research and development in the field of autonomous aerial manipulation.

Soft robots are made of compliant materials and perform tasks that are challenging for rigid robots. However, their continuum nature makes it difficult to develop model-based control strategies. This work presents a robust model-based control scheme for soft continuum robots. Our dynamic model is based on the Euler-Lagrange approach, but it uses a more accurate description of the robot's inertia and does not include oversimplified assumptions. Based on this model, we introduce an adaptive sliding mode control scheme, which is robust against model parameter uncertainties and unknown input disturbances. We perform a series of experiments with a physical soft continuum arm to evaluate the effectiveness of our controller at tracking task-space trajectory under different payloads. The tracking performance of the controller is around 38 % more accurate than that of a state-of-the-art controller, i.e., the inverse dynamics method. Moreover, the proposed model-based control design is flexible and can be generalized to any continuum robotic arm with an arbitrary number of segments. With this control strategy, soft robotic object manipulation can become more accurate while remaining robust to disturbances.

The musculoskeletal humanoid is difficult to modelize due to the flexibility and redundancy of its body, whose state can change over time, and so balance control of its legs is challenging. There are some cases where ordinary PID controls may cause instability. In this study, to solve these problems, we propose a method of learning a correlation model among the joint angle, muscle tension, and muscle length of the ankle and the zero moment point to perform balance control. In addition, information on the changing body state is embedded in the model using parametric bias, and the model estimates and adapts to the current body state by learning this information online. This makes it possible to adapt to changes in upper body posture that are not directly taken into account in the model, since it is difficult to learn the complete dynamics of the whole body considering the amount of data and computation. The model can also adapt to changes in body state, such as the change in footwear and change in the joint origin due to recalibration. The effectiveness of this method is verified by a simulation and by using an actual musculoskeletal humanoid, Musashi.

There is a way to utilize humanoid robots to mimic human behavior by taking advantage of their human-like proportions. In general, motion capture is used; in this case, the posture of the body links can be taken. However, this method does not provide detailed information on the contact state, which is important for actions that involve contact with objects. In this study, we focused on the foot, which has not been paid much attention among the parts where contact and manipulation with objects are important, and developed a device to measure the contact pressure distribution at the outer edge of the sole. We proposed an index, SS-COP, which simply reflects the contact on the curved surface of the sole for this device and a robot foot with lateral force sensation and realized a behavior that imitates the foot condition of a humanoid robot by using this index.

Legged robots with high locomotive performance have been extensively studied, and various leg structures have been proposed. Especially, a leg structure that can achieve both continuous and high jumps is advantageous for moving around in a three-dimensional environment. In this study, we propose a parallel wire-driven leg structure, which has one DoF of linear motion and two DoFs of rotation and is controlled by six wires, as a structure that can achieve both continuous jumping and high jumping. The proposed structure can simultaneously achieve high controllability on each DoF, long acceleration distance and high power required for jumping. In order to verify the jumping performance of the parallel wire-driven leg structure, we have developed a parallel wire-driven monopedal robot, RAMIEL. RAMIEL is equipped with quasi-direct drive, high power wire winding mechanisms and a lightweight leg, and can achieve a maximum jumping height of 1.6 m and a maximum of seven continuous jumps.

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.

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.

Due to their inherent compliance, soft robots are more versatile than rigid linked robots when they interact with their environment, such as object manipulation or biomimetic motion, and are considered to be the key element in introducing robots to everyday environments. Although various soft robotic actuators exist, past research has focused primarily on designing and analyzing single components. Limited effort has been made to combine each component to create an overall capable, integrated soft robot. Ideally, the behavior of such a robot can be accurately modeled, and its motion within an environment uses its proprioception, without requiring external sensors. This work presents a design and modeling process for a Soft continuum Proprioceptive Arm (SoPrA) actuated by pneumatics. The integrated design is suitable for an analytical model due to its internal capacitive flex sensor for proprioceptive measurements and its fiber-reinforced fluidic elastomer actuators. The proposed analytical dynamical model accounts for the inertial effects of the actuator’s mass and the material properties, and predicts in real-time the soft robot’s behavior. Our estimation method integrates the analytical model with proprioceptive sensors to calculate external forces, all without relying on an external motion capture system. SoPrA is validated in a series of experiments demonstrating the model’s and sensor’s accuracy in estimation. SoPrA will enable soft arm manipulation including force sensing while operating in obstructed environments that disallows exteroceptive measurements.

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.

Eye gaze is an important non-verbal behavior for communication robots as it serves as the onset of communication. Existing communication robots have various eyes because design choices for an appropriate eye have yet to be determined, so many robots are designed on the basis of individual designers' ideas. Thus, this study focuses on human-like eye gaze in a real environment. We developed an independent human-like eye gaze component called Akagachi for various robots and conducted an observational field study by implementing it to a vendor robot called Reika. We conducted a field study in a theme park where Reika sells soft-serve ice cream in a food stall and analyzed the behaviors of 984 visitors. Our results indicate that Reika elicits significantly more interaction from people with eye gaze than without it.