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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., Comment: Accepted at Humanoids2019

To develop Musashi as a musculoskeletal humanoid platform to investigate learning control systems, we aimed for a body with flexible musculoskeletal structure, redundant sensors, and easily reconfigurable structure. For this purpose, we develop joint modules that can directly measure joint angles, muscle modules that can realize various muscle routes, and nonlinear elastic units with soft structures, etc. Next, we develop MusashiLarm, a musculoskeletal platform composed of only joint modules, muscle modules, generic bone frames, muscle wire units, and a few attachments. Finally, we develop Musashi, a musculoskeletal humanoid platform which extends MusashiLarm to the whole body design, and conduct several basic experiments and learning control experiments to verify the effectiveness of its concept., Comment: Accepted at IROS2019

We develop a new kind of relative trace formulas on ${\bf PGSp}_2$ involving the Bessel periods and the Rankin-Selberg type integral a la Piatetski-Shapiro for Siegel cusp forms on its spectral side. As an application, a version of weighted equidistribution theorems for the Satake parameters of Siegel cusp forms of square-free level and of scalar weights is proved., Comment: 64 pages

This study proposed an extension of the classical kinetic theory of gases (CKTG) that incorporates the effect of an external gravitational field on the motions of molecular particles. First, we rederived the CKTG in terms of the kinetics of constituent particles to account for the effect of accelerating particles by an external gravitational field. Consequently, we obtained an extended expression for the virial pressure in molecular dynamics under an external potential field. As predicted by our theoretical model, a pressure difference in the gravitational direction was observed in our particle collision simulations. Further analysis proved that if the external potential energy of each particle is sufficiently small (but not negligible compared to its kinetic energy), a pressure difference emerges between the walls while maintaining the properties of equilibrium statistical mechanics, following the Maxwell--Boltzmann distribution. Notably, our model was formulated based on only basic knowledge of physics. In summary, this study obtained fundamental insights into the kinetic theory of gases under gravitational fields that will be useful for both education and practical applications.

The estimation and management of motor temperature are important for the continuous movements of robots. In this study, we propose an online learning method of thermal model parameters of motors for an accurate estimation of motor core temperature. Also, we propose a management method of motor core temperature using the updated model and anomaly detection method of motors. Finally, we apply this method to the muscles of the musculoskeletal humanoid and verify the ability of continuous movements., Comment: Accepted at IEEE Robotics and Automation Letters

The flexible musculoskeletal hand is difficult to modelize, and its model can change constantly due to deterioration over time, irreproducibility of initialization, etc. Also, for object recognition, contact detection, and contact control using the hand, it is desirable not to use a neural network trained for each task, but to use only one integrated network. Therefore, we develop a method to acquire a sensor state equation of the musculoskeletal hand using a recurrent neural network with parametric bias. By using this network, the hand can realize recognition of the grasped object, contact simulation, detection, and control, and can cope with deterioration over time, irreproducibility of initialization, etc. by updating parametric bias. We apply this study to the hand of the musculoskeletal humanoid Musashi and show its effectiveness., Comment: Accepted at IEEE Robotics and Automation Letters

The Kalman filter is indispensable for state estimation across diverse fields but faces computational challenges with higher dimensions. Approaches such as Riccati equation approximations aim to alleviate this complexity, yet ensuring properties like bounded errors remains challenging. Yamada and Ohki introduced low-rank Kalman-Bucy filters for continuous-time systems, ensuring bounded errors. This paper proposes a discrete-time counterpart of the low-rank filter and shows its system theoretic properties and conditions for bounded mean square error estimation. Numerical simulations show the effectiveness of the proposed method., Comment: 6 pages, presented at SICE2024

We explicitly compute the Rankin-Selberg type integral introduced by Piatetski-Shapiro over adeles for vector-valued Siegel cusp forms of square-free levels $\Gamma_0(N)$. On the way, for particular test functions in the Bessel models of irreducible admissible representations, exact evaluations of the local zeta-integrals are given., Comment: 45 pages

The flexible under-actuated musculoskeletal hand is superior in its adaptability and impact resistance. On the other hand, since the relationship between sensors and actuators cannot be uniquely determined, almost all its controls are based on feedforward controls. When grasping and using a tool, the contact state of the hand gradually changes due to the inertia of the tool or impact of action, and the initial contact state is hardly kept. In this study, we propose a system that trains the predictive network of sensor state transition using the actual robot sensor information, and keeps the initial contact state by a feedback control using the network. We conduct experiments of hammer hitting, vacuuming, and brooming, and verify the effectiveness of this study., Comment: Accepted at ICRA2020

While the musculoskeletal humanoid has various biomimetic benefits, the modeling of its complex structure is difficult, and many learning-based systems have been developed so far. There are various methods, such as control methods using acquired relationships between joints and muscles represented by a data table or neural network, and state estimation methods using Extended Kalman Filter or table search. In this study, we construct a Musculoskeletal AutoEncoder representing the relationship among joint angles, muscle tensions, and muscle lengths, and propose a unified method of state estimation, control, and simulation of musculoskeletal humanoids using it. By updating the Musculoskeletal AutoEncoder online using the actual robot sensor information, we can continuously conduct more accurate state estimation, control, and simulation than before the online learning. We conducted several experiments using the musculoskeletal humanoid Musashi, and verified the effectiveness of this study., Comment: Accepted at IEEE Robotics and Automation Letters

This paper summarizes an autonomous driving project by musculoskeletal humanoids. The musculoskeletal humanoid, which mimics the human body in detail, has redundant sensors and a flexible body structure. These characteristics are suitable for motions with complex environmental contact, and the robot is expected to sit down on the car seat, step on the acceleration and brake pedals, and operate the steering wheel by both arms. We reconsider the developed hardware and software of the musculoskeletal humanoid Musashi in the context of autonomous driving. The respective components of autonomous driving are conducted using the benefits of the hardware and software. Finally, Musashi succeeded in the pedal and steering wheel operations with recognition., Comment: Accepted at IEEE Robotics and Automation Magazine

This paper reports a three-dimensional (3D) simulation of a rotating liquid helium-4, using a two-fluid model with spin-angular momentum conservation. Our model was derived from the particle approximation of an inviscid fluid with residual viscosity. Despite the fully classical mechanical picture, the resulting system equations were consistent with those of the conventional two-fluid model. We consider bulk liquid helium-4 to be an inviscid fluid, assuming that the viscous fluid component remains at finite temperatures. As the temperature decreased, the amount of the viscous fluid component decreased, ultimately becoming a fully inviscid fluid at absolute zero. Weak compressibility is assumed to express the volume change because some helium atoms do not render fluid owing to BECs or change states because of local thermal excitation. One can solve the governing equations for an incompressible fluid using explicit SPH (smoothed particle hydrodynamics), simultaneously reproducing density fluctuations and describing the fluid in a many-particle system. We assume the following fluid-particle duality: a hydrodynamic interfacial tension between the inviscid and viscous components or a local interaction force between two types of fluid particles. The former can be induced in the horizontal direction when non-negligible non-uniformity of the particles occurs during forced two-dimensional rotation, and the latter is non-negligible when the former is negligible. We performed a largescale simulation of 3D liquid helium forced to rotate horizontally using 32 GPUs. Compared with the low-resolution calculation using 2.4 million particles, the high-resolution calculation using 19.6 million particles showed spinning vortices close to those of the theoretical solution. We obtained a promising venue to establish a practical simulation method for bulk liquid helium-4., Comment: The following article has been submitted to Physics of Fluids. After it is published, it will be found at https://pubs.aip.org/aip/pof

By the recent spread of machine learning in the robotics field, a humanoid that can act, perceive, and learn in the real world through contact with the environment needs to be developed. In this study, as one of the choices, we propose a novel humanoid TWIMP, which combines a human mimetic musculoskeletal upper limb with a two-wheel inverted pendulum. By combining the benefit of a musculoskeletal humanoid, which can achieve soft contact with the external environment, and the benefit of a two-wheel inverted pendulum with a small footprint and high mobility, we can easily investigate learning control systems in environments with contact and sudden impact. We reveal our whole concept and system details of TWIMP, and execute several preliminary experiments to show its potential ability., Comment: Accepted at Humanoids2018

The tendon-driven musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex muscle and bone structures is difficult and conventional model-based controls cannot realize intended movements. Therefore, a learning control mechanism that acquires nonlinear relationships between joint angles, muscle tensions, and muscle lengths from the actual robot is necessary. In this study, we propose a system which runs the learning control mechanism for a long time to keep the self-body image of the musculoskeletal humanoid correct at all times. Also, we show that the musculoskeletal humanoid can conduct position control, torque control, and variable stiffness control using this self-body image. We conduct a long-time self-body image acquisition experiment lasting 3 hours, evaluate variable stiffness control using the self-body image, etc., and discuss the superiority and practicality of the self-body image acquisition of musculoskeletal structures, comprehensively., Comment: Accepted at IEEE Robotics and Automation Letters, 2019

Musculoskeletal humanoids have been developed by imitating humans and expected to perform natural and dynamic motions as well as humans. To achieve desired motions stably in current musculoskeletal humanoids is not easy because they cannot maintain the sufficient moment arm of muscles in various postures. In this research, we discuss planar structures that spread across joint structures such as ligament and planar muscles and the application of planar interskeletal structures to humanoid robots. Next, we develop MusashiOLegs, a musculoskeletal legs which has planar interskeletal structures and conducts several experiments to verify the importance of planar interskeletal structures., Comment: accepted at Humanoids2020

The Kalman-Bucy filter is extensively utilized across various applications. However, its computational complexity increases significantly in large-scale systems. To mitigate this challenge, a low-rank approximated Kalman--Bucy filter was proposed, comprising Oja's principal component flow and a low-dimensional Riccati differential equation. Previously, the estimation error was confirmed solely for linear time-invariant systems with a symmetric system matrix. This study extends the application by eliminating the constraint on the symmetricity of the system matrix and describes the equilibrium points of the Oja flow along with their stability for general matrices. In addition, the domain of attraction for a set of stable equilibrium points is estimated. Based on these findings, we demonstrate that the low-rank approximated Kalman--Bucy filter with a suitable rank maintains a bounded estimation error covariance matrix if the system is controllable and observable., Comment: fixed a few typos

Integral Field Spectroscopy (IFS) is an observational method to obtain spatially resolved spectra over a specific field of view (FoV) in a single exposure. In recent years, near-infrared IFS has gained importance in observing objects with strong dust attenuation or at high redshift. One limitation of existing near-infrared IFS instruments is their relatively small FoV, less than 100 arcsec$^2$, compared to optical instruments. Therefore, we have developed a near-infrared (0.9-2.5 $\mathrm{\mu}$m) image-slicer type integral field unit (IFU) with a larger FoV of 13.5 $\times$ 10.4 arcsec$^2$ by matching a slice width to a typical seeing size of 0.4 arcsec. The IFU has a compact optical design utilizing off-axis ellipsoidal mirrors to reduce aberrations. Complex optical elements were fabricated using an ultra-precision cutting machine to achieve RMS surface roughness of less than 10 nm and a P-V shape error of less than 300 nm. The ultra-precision machining can also simplify alignment procedures. The on-sky performance evaluation confirmed that the image quality and the throughput of the IFU were as designed. In conclusion, we have successfully developed a compact IFU utilizing an ultra-precision cutting technique, almost fulfilling the requirements., Comment: 24 pages, 18 figures, 7 tables. Accepted for publication in JATIS

To detect and track structural changes in atomic nuclei, the systematic study of nuclear levels with firm spin-parity assignments is important. While linear polarization measurements have been applied to determine the electromagnetic character of gamma-ray transitions, the applicable range is strongly limited due to the low efficiency of the detection system. The multi-layer Cadmium-Telluride (CdTe) Compton camera can be a state-of-the-art gamma-ray polarimeter for nuclear spectroscopy with the high position sensitivity and the detection efficiency. We demonstrated the capability to operate this detector as a reliable gamma-ray polarimeter by using polarized 847-keV gamma rays produced by the $^{56}\rm{Fe}({\it p},{\it p'}\gamma)$ reaction. By combining the experimental data and simulated calculations, the modulation curve for the gamma ray was successfully obtained. A remarkably high polarization sensitivity was achieved, compatible with a reasonable detection efficiency. Based on the obtained results, a possible future gamma-ray polarimetery is discussed.

Nerve transmission delay is an important topic in neuroscience. Spike signals fired or received at the dendrites of a neuron travel from the axon to the presynaptic cell. The spike signal triggers a chemical reaction at the synapse, wherein a presynaptic cell transfers neurotransmitters to the postsynaptic cell, and regenerates electrical signals by a chemical reaction process through ion channels and transmits it to neighboring neurons. In the context of describing the complex physiological reaction process as a stochastic process, this study aimed to show that the distribution of the maximum time interval of spike signals follows extreme order statistics. By considering the statistical variance in the time constant of the Leaky Integrate-and-Fire model, which is a deterministic time evolution model of spike signals, we enabled randomness in the time interval of spike signals. When the time constant follows an exponential distribution function, the time interval of the spike signal also follows an exponential distribution. In this case, our theory and simulations confirmed that the histogram of the maximum time interval follows the Gumbel distribution, which is one of the three types of extreme value statistics. We also confirmed that the histogram of the maximum time interval follows a Fr\'{e}chet distribution when the time interval of the spike signal follows a Pareto distribution. These findings confirm that nerve transmission delay can be described using extreme value statistics and could, therefore, be used as a new indicator for transmission delay.

It was recently discovered that the time correlations of repeating fast radio bursts (FRBs) are similar to earthquake aftershocks. Motivated by the association between FRBs and magnetars, here we report correlation function analyses in the time-energy space for the 563 periodic radio pulses and the 579 X-ray short bursts from the magnetar SGR 1935+2154, which is known to have generated FRBs. Although radio pulses are concentrated near the fixed phase of the rotational cycle, we find that when multiple pulses occur within a single cycle, their correlation properties (aftershock production probability, aftershock rate decaying in power of time, and more) are similar to those of extragalactic FRBs and earthquakes. A possible interpretation is that the radio pulses are produced by rupture of the neutron star crust, and the first pulse within one cycle is triggered by external force periodically exerted on the crust. The periodic external force may be from the interaction of the magnetosphere with material ejected in an outburst. For X-ray bursts, we found no significant correlation signal, though correlation on the same time scale as radio pulses may be hidden due to the long event duration. The aftershock similarity between the periodic radio pulsation and FRBs is surprising, given that the two are energetically very different, and therefore the energy sources would be different. This suggests that the essence of FRB-like phenomena is starquakes, regardless of the energy source, and it is important to search for FRB-like bursts from neutron stars with various properties or environments., Comment: 9 pages, 7 figures. Accepted by MNRAS

We propose a numerical procedure for computing the prices of forward contracts, in which the underlying asset price is a Markovian local martingale. If the underlying process is a strict local martingale, multiple solutions exist for the corresponding Black-Scholes equations, and the derivative prices are characterized as the minimal solutions. When applying numerical methods that are set up on a finite grid, such as finite difference methods, additional spatial boundary conditions are required, which causes the solution to uniquely exist. In this case, the problem is reduced to specifying the boundary conditions. Our boundary condition is based on an expression of the derivative price at infinity in terms of those at finite values, and incorporates volatility behaviors beyond the boundary. The proposed procedure is demonstrated through numerical tests, which show that it outperforms the methods proposed in the literature.

We developed a 2-mm-thick CdTe double-sided strip detector (CdTe-DSD) with a 250 um strip pitch, which has high spatial resolution with a uniform large imaging area of 10 cm$^2$ and high energy resolution with high detection efficiency in tens to hundreds keV. The detector can be employed in a wide variety of fields for quantitative observations of hard X-ray and soft gamma-ray with spectroscopic imaging, for example, space observation, nuclear medicine, and non-destructive elemental analysis. This detector is thicker than the 0.75-mm-thick one previously developed by a factor of $\sim$2.7, thus providing better detection efficiency for hard X-rays and soft gamma rays. The increased thickness could potentially enhance bias-induced polarization if we do not apply sufficient bias and if we do not operate at a low temperature, but the polarization is not evident in our detector when a high voltage of 500 V is applied to the CdTe diode and the temperature is maintained at -20 $^\circ$C during one-day experiments. The ''Depth Of Interaction'' (DOI) dependence due to the CdTe diode's poor carrier-transport property is also more significant, resulting in much DOI information while complicated detector responses such as charge sharings or low-energy tails that exacerbate the loss in the energy resolution. In this paper, we developed 2-mm-thick CdTe-DSDs, studied their response, and evaluated their energy resolution, spatial resolution, and uniformity. We also constructed a theoretical model to understand the detector response theoretically, resulting in reconstructing the DOI with an accuracy of 100 um while estimating the carrier-transport property. We realized the detector that has high energy resolution and high 3D spatial resolution with a uniform large imaging area., Comment: 13 pages, 11 figures, 1 table, Accepted for publication in NIM A