arXiv subjects

Hiroshi Ito

Publications and source records attributed to Hiroshi Ito.

At least 19 recordsLinked to original sources

arXiv2026

A curvature-based criterion for harmonic circadian waveforms

Experimental and theoretical studies of circadian rhythms have focused largely on the period, on mutants that alter it and on phase shifts, and this focus has driven the identification of clock genes and clarified how clocks entrain to light--dark cycles. The waveform of the oscillation itself, by contrast, has attracted little attention as an indicator of the properties of the underlying oscillator. To assess the waveform directly, we focus on whether the trajectory in the plane spanned by a variable and its time derivative possesses an inflection point, and we define an oscillation to be harmonic when no inflection point is present. Bioluminescence recordings from cyanobacteria and from the mammalian SCN were harmonic in this sense, as were most of the core clock components in mathematical models of the circadian clock. Numerical analysis of the Goodwin model, a minimal representation of the core circadian oscillator, yielded harmonic oscillation throughout. We confirmed this numerical trend semi-analytically using a piecewise-linearized Goodwin model. Because it evaluates the properties of a waveform without assuming a model structure, the approach we propose offers a new perspective on the waveform analysis of biological rhythms in general, not only circadian ones.

q-bio.QM↗

arXiv2026

First measurement of flux of the neutron background induced by accelerated neutrinos at the J-PARC facility

This article reports the measurement of the neutron background flux accidentally induced by the neutrino beam at the J-PARC facility in Japan. In particular, neutrino-nucleus neutral-current quasi-elastic (NCQE) scattering events, $ν~(\barν)+\rm{N}\toν~(\barν)+\rm{N'}$, can be obscured by a massive background of neutron-nucleus scattering events. The neutrons are produced within the materials (such as sand and concrete) located between the beam dump and the experimental area where the detectors are placed. We measured the accidental neutron events at the J-PARC neutrino facility using BGO and liquid scintillation detectors, with a plastic scintillation detector serving as an active veto counter. Based on a neutrino-mode data set of $2.972 \times 10^{20}$ POT, we observed 88 neutron-induced recoil proton events within an electron-equivalent recoil energy range of 0.98-11.60 $\rm MeV_{ee}$, selected via pulse-shape discrimination in the liquid scintillation detector. Accounting for the detection efficiency and resolution, and through comparison with simulations, the neutron flux was determined to be$[1.45^{+0.22}_{-0.24}~\rm{(stat.)} \pm 0.55~\rm{(sys.)}] \times 10^{-7}~\rm{cm}^{-2} \rm{s}^{-1}\rm{POT}^{-1}$,assuming an exponential neutron energy spectrum. This result will contribute to the evaluation of neutron backgrounds for neutrino experiments at the J-PARC facility.

physics.ins-det↗

arXiv2026

Circadian output network can buffer period variability

Circadian rhythms are biological oscillations that govern 24-hour physiological and behavioral processes across most organisms. Recent bioimaging studies have revealed that even individual cells can exhibit circadian rhythms. The period of cellular oscillations can fluctuate due to molecular noise in the circadian clock machinery. Whether regulatory networks downstream of the clock amplify or attenuate clock-derived period fluctuations remains poorly understood. In this study, we numerically observed period variability in a self-sustained oscillator coupled to an output network. Our numerical calculations demonstrated that a serial pathway does not merely relay timing signals but actively shapes rhythmic reliability. The extent of this reduction depended on parameters of both the clock and output systems. For more complex output networks, the shortest-path length from the core oscillator was a major determinant of increased oscillation precision. This noise-buffering effect saturated in long cascades. These results suggest the existence of an intrinsic precision-enhancing mechanism embedded within circadian output networks.

q-bio.MN↗

arXiv2026

Stereo Multistage Spatial Attention for Real-Time Mobile Manipulation Under Visual Scale Variation and Disturbances

Robots operating in open, unstructured real-world environments must rely on onboard visual perception while autonomously moving across different locations. Continuous changes in onboard camera viewpoints cause significant visual scale variations in target objects, affecting vision-based motion generation. In this work, we present a stereo multistage spatial attention-based deep predictive learning method for real-time mobile manipulation. The proposed methods extracts task-relevant spatial attention points from stereo images and integrates them with robot states through a hierarchical recurrent architecture for closed-loop action prediction. We evaluate the system on four real-world mobile manipulation tasks using a mobile manipulator, including rigid placement, articulated object manipulation, and deformable object interaction. Experiments under randomized initial positions and visual disturbance conditions demonstrate improved robustness and task success rates compared to representative imitation learning and vision-language-action baselines under identical control settings. The results indicate that structured stereo spatial attention combined with predictive temporal modeling provides an effective solution within the evaluated mobile manipulation scenarios.

cs.RO↗

arXiv2026

Solar Neutrino Flux Fluctuations Caused by Solar Gravity Modes

We have evaluated fluctuations in neutrino fluxes caused by solar gravity (g) modes based on the analysis of linear adiabatic oscillation of a spherically symmetric star. We find that the first-order fluctuation is zero due to geometrical cancellation. We still find that the second-order fluctuation is non-zero, which consists of time-varying and non-time-varying components. The amplitude of the time-varying component is small (${\sim} 10^{-9}$ in relative difference, in the case of $\mathrm{^{8}B}$ neutrino) and well below the detection limits of the current neutrino detectors, when we assume the g-mode amplitude parameter $A_{n \ell}$ to be $10^{-5}$, which corresponds to the assumed maximum relative temperature perturbation inside the Sun. Thus, it is at the moment fair to say that detecting individual solar g-modes via the solar neutrino flux measurement is almost impossible. However, the net increase in the mean neutrino flux that originates from the non-time-varying component could be non-negligible. In particular, since $A_{n \ell}$ may be related to convection amplitude, which could change in accordance with the solar magnetic activity, the total net increase in the neutrino flux, which is proportional to $A_{n \ell}^2$, should also change with the solar activity cycle. Such a long-period variation~(${\sim} 11$~years) in the neutrino flux could thus be interpreted as evidence for a bunch of solar g-modes. Comparison of the theoretical prediction with the solar neutrino measurements by, e.g., Super-Kamiokande, may have a potential to put constraints on the theory of the excitation mechanism of solar g-modes.

astro-ph.SR↗

arXiv2026

Terahertz Synthetic FM Triplet for Distortion-Free Stabilization and Lamb-Dip Spectroscopy

We demonstrate a distortion-free terahertz frequency stabilization technique using a "synthetic FM triplet" to overcome modulation sideband interference associated with high-density spectral lines in molecular clocks. By applying this method to the rotational transitions of acetonitrile (CH$_3$CN), we successfully generated clean derivative waveforms free from inter-line interference, achieving a fractional frequency instability of $1 \times 10^{-9}$ at an averaging time of $1~\mathrm{s}$. Furthermore, we report the observation of Lamb-dips using this high-fidelity approach. Our results establish acetonitrile as a promising candidate for high-agility molecular clocks and provide a robust solution for precision spectroscopy of molecules with complex hyperfine structures.

physics.optics↗

arXiv2025

UF-RNN: Real-Time Adaptive Motion Generation Using Uncertainty-Driven Foresight Prediction

Training robots to operate effectively in environments with uncertain states, such as ambiguous object properties or unpredictable interactions, remains a longstanding challenge in robotics. Imitation learning methods typically rely on successful examples and often neglect failure scenarios where uncertainty is most pronounced. To address this limitation, we propose the Uncertainty-driven Foresight Recurrent Neural Network (UF-RNN), a model that combines standard time-series prediction with an active "Foresight" module. This module performs internal simulations of multiple future trajectories and refines the hidden state to minimize predicted variance, enabling the model to selectively explore actions under high uncertainty. We evaluate UF-RNN on a door-opening task in both simulation and a real-robot setting, demonstrating that, despite the absence of explicit failure demonstrations, the model exhibits robust adaptation by leveraging self-induced chaotic dynamics in its latent space. When guided by the Foresight module, these chaotic properties stimulate exploratory behaviors precisely when the environment is ambiguous, yielding improved success rates compared to conventional stochastic RNN baselines. These findings suggest that integrating uncertainty-driven foresight into imitation learning pipelines can significantly enhance a robot's ability to handle unpredictable real-world conditions.

cs.RO↗

arXiv2025

A3RNN: Bi-directional Fusion of Bottom-up and Top-down Process for Developmental Visual Attention in Robots

This study investigates the developmental interaction between top-down (TD) and bottom-up (BU) visual attention in robotic learning. Our goal is to understand how structured, human-like attentional behavior emerges through the mutual adaptation of TD and BU mechanisms over time. To this end, we propose a novel attention model $A^3 RNN$ that integrates predictive TD signals and saliency-based BU cues through a bi-directional attention architecture. We evaluate our model in robotic manipulation tasks using imitation learning. Experimental results show that attention behaviors evolve throughout training, from saliency-driven exploration to prediction-driven direction. Initially, BU attention highlights visually salient regions, which guide TD processes, while as learning progresses, TD attention stabilizes and begins to reshape what is perceived as salient. This trajectory reflects principles from cognitive science and the free-energy framework, suggesting the importance of self-organizing attention through interaction between perception and internal prediction. Although not explicitly optimized for stability, our model exhibits more coherent and interpretable attention patterns than baselines, supporting the idea that developmental mechanisms contribute to robust attention formation.

cs.RO↗

arXiv2025

AIRoA MoMa Dataset: A Large-Scale Hierarchical Dataset for Mobile Manipulation

As robots transition from controlled settings to unstructured human environments, building generalist agents that can reliably follow natural language instructions remains a central challenge. Progress in robust mobile manipulation requires large-scale multimodal datasets that capture contact-rich and long-horizon tasks, yet existing resources lack synchronized force-torque sensing, hierarchical annotations, and explicit failure cases. We address this gap with the AIRoA MoMa Dataset, a large-scale real-world multimodal dataset for mobile manipulation. It includes synchronized RGB images, joint states, six-axis wrist force-torque signals, and internal robot states, together with a novel two-layer annotation schema of sub-goals and primitive actions for hierarchical learning and error analysis. The initial dataset comprises 25,469 episodes (approx. 94 hours) collected with the Human Support Robot (HSR) and is fully standardized in the LeRobot v2.1 format. By uniquely integrating mobile manipulation, contact-rich interaction, and long-horizon structure, AIRoA MoMa provides a critical benchmark for advancing the next generation of Vision-Language-Action models. The first version of our dataset is now available at https://huggingface.co/datasets/airoa-org/airoa-moma .

cs.RO↗

arXiv2025

Learning Multimodal Attention for Manipulating Deformable Objects with Changing States

To support humans in their daily lives, robots are required to autonomously learn, adapt to objects and environments, and perform the appropriate actions. We tackled on the task of cooking scrambled eggs using real ingredients, in which the robot needs to perceive the states of the egg and adjust stirring movement in real time, while the egg is heated and the state changes continuously. In previous works, handling changing objects was found to be challenging because sensory information includes dynamical, both important or noisy information, and the modality which should be focused on changes every time, making it difficult to realize both perception and motion generation in real time. We propose a predictive recurrent neural network with an attention mechanism that can weigh the sensor input, distinguishing how important and reliable each modality is, that realize quick and efficient perception and motion generation. The model is trained with learning from the demonstration, and allows the robot to acquire human-like skills. We validated the proposed technique using the robot, Dry-AIREC, and with our learning model, it could perform cooking eggs with unknown ingredients. The robot could change the method of stirring and direction depending on the status of the egg, as in the beginning it stirs in the whole pot, then subsequently, after the egg started being heated, it starts flipping and splitting motion targeting specific areas, although we did not explicitly indicate them.

cs.RO↗

arXiv2025

Input-gated Bilateral Teleoperation: An Easy-to-implement Force Feedback Teleoperation Method for Low-cost Hardware

Effective data collection in contact-rich manipulation requires force feedback during teleoperation, as accurate perception of contact is crucial for stable control. However, such technology remains uncommon, largely because bilateral teleoperation systems are complex and difficult to implement. To overcome this, we propose a bilateral teleoperation method that relies only on a simple feedback controller and does not require force sensors. The approach is designed for leader-follower setups using low-cost hardware, making it broadly applicable. Through numerical simulations and real-world experiments, we demonstrate that the method requires minimal parameter tuning, yet achieves both high operability and contact stability, outperforming conventional approaches. Furthermore, we show its high robustness: even at low communication cycle rates between leader and follower, control performance degradation is minimal compared to high-speed operation. We also prove our method can be implemented on two types of commercially available low-cost hardware with zero parameter adjustments. This highlights its high ease of implementation and versatility. We expect this method will expand the use of force feedback teleoperation systems on low-cost hardware. This will contribute to advancing contact-rich task autonomy in imitation learning.

cs.RO↗

arXiv2025

Lamb-dip spectroscopy of rotational levels with UTC-PD terahertz emitter

Pump-probe saturation spectroscopy in the sub-terahertz region was performed in the rotational transition (J, K) = (16, 0) <- (15, 0) for gas-phase acetonitrile molecules in the counter-propagating configuration. We observed Lamb-dips at much lower excitation powers than previously reported. The linewidth in the zero-pressure limit was 10 kHz, which was estimated from the intensity and pressure dependence. This corresponds to the transit-time broadening.

physics.optics↗

arXiv2024

Adaptive Motion Generation Using Uncertainty-Driven Foresight Prediction

Uncertainty of environments has long been a difficult characteristic to handle, when performing real-world robot tasks. This is because the uncertainty produces unexpected observations that cannot be covered by manual scripting. Learning based robot controlling methods are a promising approach for generating flexible motions against unknown situations, but still tend to suffer under uncertainty due to its deterministic nature. In order to adaptively perform the target task under such conditions, the robot control model must be able to accurately understand the possible uncertainty, and to exploratively derive the optimal action that minimizes such uncertainty. This paper extended an existing predictive learning based robot control method, which employ foresight prediction using dynamic internal simulation. The foresight module refines the model's hidden states by sampling multiple possible futures and replace with the one that led to the lower future uncertainty. The adaptiveness of the model was evaluated on a door opening task. The door can be opened either by pushing, pulling, or sliding, but robot cannot visually distinguish which way, and is required to adapt on the fly. The results showed that the proposed model adaptively diverged its motion through interaction with the door, whereas conventional methods failed to stably diverge. The models were analyzed on Lyapunov exponents of RNN hidden states which reflect the possible divergence at each time step during task execution. The result indicated that the foresight module biased the model to consider future consequences, which lead to embedding uncertainties at the policy of the robot controller, rather than the resultant observation. This is beneficial for implementing adaptive behaviors, which indices derivation of diverse motion during exploration.

cs.RO↗

arXiv2024

Achieving Faster and More Accurate Operation of Deep Predictive Learning

Achieving both high speed and precision in robot operations is a significant challenge for social implementation. While factory robots excel at predefined tasks, they struggle with environment-specific actions like cleaning and cooking. Deep learning research aims to address this by enabling robots to autonomously execute behaviors through end-to-end learning with sensor data. RT-1 and ACT are notable examples that have expanded robots' capabilities. However, issues with model inference speed and hand position accuracy persist. High-quality training data and fast, stable inference mechanisms are essential to overcome these challenges. This paper proposes a motion generation model for high-speed, high-precision tasks, exemplified by the sports stacking task. By teaching motions slowly and inferring at high speeds, the model achieved a 94% success rate in stacking cups with a real robot.

cs.RO↗

arXiv2024

Visual Spatial Attention and Proprioceptive Data-Driven Reinforcement Learning for Robust Peg-in-Hole Task Under Variable Conditions

Anchor-bolt insertion is a peg-in-hole task performed in the construction field for holes in concrete. Efforts have been made to automate this task, but the variable lighting and hole surface conditions, as well as the requirements for short setup and task execution time make the automation challenging. In this study, we introduce a vision and proprioceptive data-driven robot control model for this task that is robust to challenging lighting and hole surface conditions. This model consists of a spatial attention point network (SAP) and a deep reinforcement learning (DRL) policy that are trained jointly end-to-end to control the robot. The model is trained in an offline manner, with a sample-efficient framework designed to reduce training time and minimize the reality gap when transferring the model to the physical world. Through evaluations with an industrial robot performing the task in 12 unknown holes, starting from 16 different initial positions, and under three different lighting conditions (two with misleading shadows), we demonstrate that SAP can generate relevant attention points of the image even in challenging lighting conditions. We also show that the proposed model enables task execution with higher success rate and shorter task completion time than various baselines. Due to the proposed model's high effectiveness even in severe lighting, initial positions, and hole conditions, and the offline training framework's high sample-efficiency and short training time, this approach can be easily applied to construction.

cs.RO↗

arXiv2024

Development of a low-background micro pixel chamber for directional dark matter searches

Direct detection of weakly interacting massive particles (WIMPs) can provide strong evidence of their existence and the directional method would have an advantage over other methods to detect the clear signal of WIMPs. Time projection chambers with micro-patterned gaseous detectors (MPGDs) are one of the common devices used in directional WIMP searches. A micro pixel chamber ($μ$-PIC), one of the various types of MPGDs, with specially selected low background materials (LBG$μ$-PIC) was developed and its performance was studied. The radon emission of the LBG$μ$-PIC was less than 1/60 of that of the $μ$-PIC currently in use. Although a non-negligible gain non-homogeneity was seen for the LBG$μ$-PIC, it can be used for the directional WIMP search with the correction of the non-homogeneity.

physics.ins-det↗

arXiv2024

Deep Predictive Learning: Motion Learning Concept inspired by Cognitive Robotics

Bridging the gap between motion models and reality is crucial by using limited data to deploy robots in the real world. Deep learning is expected to be generalized to diverse situations while reducing feature design costs through end-to-end learning for environmental recognition and motion generation. However, data collection for model training is costly, and time and human resources are essential for robot trial-and-error with physical contact. We propose "Deep Predictive Learning," a motion learning concept that predicts the robot's sensorimotor dynamics, assuming imperfections in the prediction model. The predictive coding theory inspires this concept to solve the above problems. It is based on the fundamental strategy of predicting the near-future sensorimotor states of robots and online minimization of the prediction error between the real world and the model. Based on the acquired sensor information, the robot can adjust its behavior in real time, thereby tolerating the difference between the learning experience and reality. Additionally, the robot was expected to perform a wide range of tasks by combining the motion dynamics embedded in the model. This paper describes the proposed concept, its implementation, and examples of its applications in real robots. The code and documents are available at: https://ogata-lab.github.io/eipl-docs

cs.RO↗

arXiv2023

Generalized equilibria for color-gradient lattice Boltzmann model based on higher-order Hermite polynomials: A simplified implementation with central moments

We propose generalized equilibria of a three-dimensional color-gradient lattice Boltzmann model for two-component two-phase flows using higher-order Hermite polynomials. Although the resulting equilibrium distribution function, which includes a sixth-order term on the velocity, is computationally cumbersome, its equilibrium central moments (CMs) are velocity-independent and have a simplified form. Numerical experiments show that our approach, as in Wen et al. [Phys. Rev. E 100, 023301 (2019)] who consider terms up to third order, improves the Galilean invariance compared to that of the conventional approach. Dynamic problems can be solved with high accuracy at a density ratio of 10; however, the accuracy is still limited to a density ratio of $1\,000$. For lower density ratios, the generalized equilibria benefit from the CM-based multiple-relaxation-time model, especially at very high Reynolds numbers, significantly improving the numerical stability.

physics.comp-ph↗