arXiv subjects

Shoichi Hasegawa

Publications and source records attributed to Shoichi Hasegawa.

At least 19 recordsLinked to original sources

arXiv2026

First Measurement of the $K^-$ Escape Cross Section in the ${}^{12}{\rm C}(K^{-},p)$ Reaction

We investigated the $\bar{K}$-nucleus interaction through the simultaneous measurement of the inclusive $^{12}{\rm C}(K^-, p)$ and exclusive $K^-$-escape $^{12}{\rm C}(K^-, p K^-_{esc})$ reactions at $1.8$ GeV/$c$ at J-PARC. The present measurement explicitly focuses on the $K^-$ escape process for the first time, successfully accomplishing a direct experimental determination of the imaginary part of the $K^-$ optical potential. The differential cross section for the $K^-$-escape reaction was determined to be $436 \pm 6\:(\text{stat.}) \pm 44\:(\text{syst.})~μ\text{b/sr}$. A simultaneous likelihood fit yielded real and imaginary potential strengths of $V_0 = -72\:^{+3}_{-5}\:(\text{stat.})\:^{+0}_{-8}\:(\text{syst.})~\text{MeV}$ and $W_0 = -100\:^{+7}_{-1}\:(\text{stat.})\:^{+0}_{-16}\:(\text{syst.})~\text{MeV}$ at the nuclear center, respectively. The derived $W_0$ is significantly stronger than that predicted by theoretical models based on one-nucleon processes, suggesting possible contribution of multi-nucleon involving processes.

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arXiv2026

On-Demand Human Assistance for Task Continuation under Physical Action Failures in LLM-based Planning

While robot action planning based on large language models (LLMs) has advanced remarkably, continuing a task after a physical action failure during execution remains a key challenge. For example, after failing to grasp an object, the robot may not notice that it has fallen off the table and keep trying to detect and pick it up, stalling the task. Existing approaches either rely on closed-loop autonomous re-planning, which fails when errors exceed the robot's capabilities, or on human intervention without updating the LLM's plan, hindering subsequent planning. We present a system for LLM-based robot action planning that integrates remote human intervention with feedback-based replanning. When the LLM detects an action failure, the robot requests remote assistance; an operator resolves the failure through teleoperation and reports the outcome in natural language, which is fed back to the LLM to update its plan so that the task can continue. We evaluated the proposed system on a real-world trash-collection task, comparing it with a fully autonomous method and a teleoperation-only baseline. The proposed system improves task progress over the autonomous method in this case study. For easy-to-retrieve trash, the gap from the teleoperation reference was small, while a larger gap remained for harder-to-retrieve trash. These results suggest that, in this case study, combining remote human intervention with feedback-based replanning was associated with continued task execution even in the face of otherwise unrecoverable failures. The project website is https://emergentsystemlabstudent.github.io/REPAIR/.

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arXiv2026

Whose Is This?: Context-Aware Object Ownership Inference with Uncertainty-Guided Questioning

Service robots must infer object ownership to correctly interpret instructions such as "bring me my cup." However, ownership is a latent attribute that cannot be directly observed, and existing methods often rely on limited cues such as recent usage, making them unreliable in scenarios such as temporary sharing. We propose a framework for context-aware ownership inference with uncertainty-guided interaction (COIN). The method integrates user background information and object usage history using a large language model (LLM) to estimate ownership scores. To handle uncertainty, we apply conformal prediction to construct a set of plausible owners and selectively generate user queries when the prediction is uncertain. Experiments in a simulated home environment show that the proposed method consistently outperforms baseline approaches, achieving a Subset Accuracy of 0.988 and a Mean Jaccard index of 0.991. The method also maintains high performance in scenarios involving temporary use and shared ownership. The results demonstrate that combining contextual reasoning with uncertainty-aware interaction improves both estimation accuracy and robustness. The project page is available at https://emergentsystemlabstudent.github.io/COIN/.

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arXiv2025

Multi-Robot Task Planning for Multi-Object Retrieval Tasks with Distributed On-Site Knowledge via Large Language Models

It is crucial to efficiently execute instructions such as "Find an apple and a banana" or "Get ready for a field trip," which require searching for multiple objects or understanding context-dependent commands. This study addresses the challenging problem of determining which robot should be assigned to which part of a task when each robot possesses different situational on-site knowledge-specifically, spatial concepts learned from the area designated to it by the user. We propose a task planning framework that leverages large language models (LLMs) and spatial concepts to decompose natural language instructions into subtasks and allocate them to multiple robots. We designed a novel few-shot prompting strategy that enables LLMs to infer required objects from ambiguous commands and decompose them into appropriate subtasks. In our experiments, the proposed method achieved 47/50 successful assignments, outperforming random (28/50) and commonsense-based assignment (26/50). Furthermore, we conducted qualitative evaluations using two actual mobile manipulators. The results demonstrated that our framework could handle instructions, including those involving ad hoc categories such as "Get ready for a field trip," by successfully performing task decomposition, assignment, sequential planning, and execution.

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arXiv2025

Community Analysis of Social Virtual Reality Based on Large-Scale Log Data of a Commercial Metaverse Platform

This study quantitatively analyzes the structural characteristics of user communities within Social Virtual Reality (Social VR) platforms supporting head-mounted displays (HMDs), based on large-scale log data. By detecting and evaluating community structures from data on substantial interactions (defined as prolonged co-presence in the same virtual space), we found that Social VR platforms tend to host numerous, relatively small communities characterized by strong internal cohesion and limited inter-community connections. This finding contrasts with the large-scale, broadly connected community structures typically observed in conventional Social Networking Services (SNS). Furthermore, we identified a user segment capable of mediating between communities, despite these users not necessarily having numerous direct connections. We term this user segment `community hoppers' and discuss their characteristics. These findings contribute to a deeper understanding of the community structures that emerge within the unique communication environment of Social VR and the roles users play within them.

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arXiv2025

Toward Ownership Understanding of Objects: Active Question Generation with Large Language Model and Probabilistic Generative Model

Robots operating in domestic and office environments must understand object ownership to correctly execute instructions such as ``Bring me my cup.'' However, ownership cannot be reliably inferred from visual features alone. To address this gap, we propose Active Ownership Learning (ActOwL), a framework that enables robots to actively generate and ask ownership-related questions to users. ActOwL employs a probabilistic generative model to select questions that maximize information gain, thereby acquiring ownership knowledge efficiently to improve learning efficiency. Additionally, by leveraging commonsense knowledge from Large Language Models (LLM), objects are pre-classified as either shared or owned, and only owned objects are targeted for questioning. Through experiments in a simulated home environment and a real-world laboratory setting, ActOwL achieved significantly higher ownership clustering accuracy with fewer questions than baseline methods. These findings demonstrate the effectiveness of combining active inference with LLM-guided commonsense reasoning, advancing the capability of robots to acquire ownership knowledge for practical and socially appropriate task execution.

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arXiv2025

Take That for Me: Multimodal Exophora Resolution with Interactive Questioning for Ambiguous Out-of-View Instructions

Daily life support robots must interpret ambiguous verbal instructions involving demonstratives such as ``Bring me that cup,'' even when objects or users are out of the robot's view. Existing approaches to exophora resolution primarily rely on visual data and thus fail in real-world scenarios where the object or user is not visible. We propose Multimodal Interactive Exophora resolution with user Localization (MIEL), which is a multimodal exophora resolution framework leveraging sound source localization (SSL), semantic mapping, visual-language models (VLMs), and interactive questioning with GPT-4o. Our approach first constructs a semantic map of the environment and estimates candidate objects from a linguistic query with the user's skeletal data. SSL is utilized to orient the robot toward users who are initially outside its visual field, enabling accurate identification of user gestures and pointing directions. When ambiguities remain, the robot proactively interacts with the user, employing GPT-4o to formulate clarifying questions. Experiments in a real-world environment showed results that were approximately 1.3 times better when the user was visible to the robot and 2.0 times better when the user was not visible to the robot, compared to the methods without SSL and interactive questioning. The project website is https://emergentsystemlabstudent.github.io/MIEL/.

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arXiv2025

Public Evaluation on Potential Social Impacts of Fully Autonomous Cybernetic Avatars for Physical Support in Daily-Life Environments: Large-Scale Demonstration and Survey at Avatar Land

Cybernetic avatars (CAs) are key components of an avatar-symbiotic society, enabling individuals to overcome physical limitations through virtual agents and robotic assistants. While semi-autonomous CAs intermittently require human teleoperation and supervision, the deployment of fully autonomous CAs remains a challenge. This study evaluates public perception and potential social impacts of fully autonomous CAs for physical support in daily life. To this end, we conducted a large-scale demonstration and survey during Avatar Land, a 19-day public event in Osaka, Japan, where fully autonomous robotic CAs, alongside semi-autonomous CAs, performed daily object retrieval tasks. Specifically, we analyzed responses from 2,285 visitors who engaged with various CAs, including a subset of 333 participants who interacted with fully autonomous CAs and shared their perceptions and concerns through a survey questionnaire. The survey results indicate interest in CAs for physical support in daily life and at work. However, concerns were raised regarding task execution reliability. In contrast, cost and human-like interaction were not dominant concerns. Project page: https://lotfielhafi.github.io/FACA-Survey/.

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arXiv2025

Lightweight Fingernail Haptic Device: Unobstructed Fingerpad Force and Vibration Feedback for Enhanced Virtual Dexterous Manipulation

This study presents a lightweight, wearable fingertip haptic device that provides physics-based haptic feedback for dexterous manipulation in virtual environments without hindering real-world interactions. The device, designed with thin strings and actuators attached to the fingernails, ensures minimal weight (1.55 g per finger) and preserves finger flexibility. Integrating the software with a physics engine renders multiple types of haptic feedback (grip force, collision, and sliding vibration feedback). We evaluated the device's performance in pressure perception, slip feedback, typical dexterous manipulation tasks, and daily operations, and we gathered user experience through subjective assessments. Our results show that participants could perceive and respond to pressure and vibration feedback. Through dexterous manipulation experiments, we further demonstrated that these minimal haptic cues significantly improved virtual task efficiency, showcasing how lightweight haptic feedback can enhance manipulation performance without complex mechanisms. The device's ability to preserve tactile sensations and minimize hindrance to real-world operations is a key advantage over glove-type haptic devices. This research offers a potential solution for designing haptic interfaces that balance lightweight construction, haptic feedback for dexterous manipulation, and daily wearability.

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arXiv2025

Cross section Measurements for $^{12}$C$(K^-, K^+Ξ^-)$ and $^{12}$C$(K^-, K^+ΛΛ)$ Reactions at 1.8 GeV$/c$

We present a measurement of the production of $Ξ^-$ and $ΛΛ$ in the $^{12}$C$(K^-, K^+)$ reaction at an incident beam momentum of 1.8 GeV/$\mathit{c}$, based on high-statistics data from J-PARC E42. The cross section for the $^{12}$C$(K^-, K^+Ξ^-)$ reaction, compared to the inclusive $^{12}$C$(K^-, K^+)$ reaction cross section, indicates that the $Ξ^-$ escaping probability peaks at 70\% in the energy region of $E_Ξ=$100 to 150 MeV above the $Ξ^-$ emission threshold. A classical approach using eikonal approximation shows that the total cross sections for $Ξ^-$ inelastic scattering ranges between 42 mb and 23 mb in the $Ξ^-$ momentum range from 0.4 to 0.6 GeV/c. Furthermore, based on the relative cross section for the $^{12}$C$(K^-, K^+ΛΛ)$ reaction, the total cross section for $Ξ^-p\toΛΛ$ is estimated in the same approach to vary between 2.2 mb and 1.0 mb in the momentum range of 0.40 to 0.65 GeV/c. Specifically, a cross section of 1.0 mb in the momentum range of 0.5 to 0.6 GeV/c imposes a constraint on the upper bound of the decay width of the $Ξ^-$ particle in infinite nuclear matter, revealing $Γ_Ξ< \sim 0.6$ MeV.

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arXiv2024

Fingernail-Based Tangential Force Simulation for Enhanced Dexterous Manipulation in Virtual Reality

This study introduces a novel haptic device for enhancing dexterous manipulation in virtual reality. By stimulating mechanoreceptors on both sides of the fingernail, our lightweight system simulates tangential force sensations. We employ mechanical stimulation for more natural tactile feedback. A preliminary "balancing grasp challenge" experiment shows that users make more frequent micro-adjustments with our device, indicating improved precision. This research aims to advance haptic feedback in VR, potentially leading to more immersive and realistic virtual interactions.

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arXiv2024

DynaPain: Moving Flame Beetle with Dynamic Pain Illusion Adapting Apparent Movement to Thermal Grill Illusion

Pain sensation presentation with movable sensory position is important to imitate the pain caused by objects in motion and the pain corresponding to a person's movements. We aimed at proposing a novel dynamic pain sensation experience, called DynaPain. DynaPain was achieved by the non-contact thermal grill illusion and the apparent movement. The demonstration provided the dynamic heat and pain experience through interaction with a flame beetle moving on the arm.

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arXiv2024

HeatFlicker: A Virtual Campfire System Utilizing Flickering Thermal Illusions by Asymmetric Vibrations

In recent years, thermal feedback has emerged as a significant sensory modality in virtual reality. However, the concept of conveying the sensation of thermal movement remains largely unexplored. We propose HeatFlicker, a virtual campfire device that recreates the flickering of fire by using a thermal illusion of moving heat identified in preliminary experiments. This device creates the illusion of heat moving from a fixed heat source. In our demonstration, we provide a novel thermal experience by simulating the flickering of a real fire.

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arXiv2024

Real-world Instance-specific Image Goal Navigation: Bridging Domain Gaps via Contrastive Learning

Improving instance-specific image goal navigation (InstanceImageNav), which locates the identical object in a real-world environment from a query image, is essential for robotic systems to assist users in finding desired objects. The challenge lies in the domain gap between low-quality images observed by the moving robot, characterized by motion blur and low-resolution, and high-quality query images provided by the user. Such domain gaps could significantly reduce the task success rate but have not been the focus of previous work. To address this, we propose a novel method called Few-shot Cross-quality Instance-aware Adaptation (CrossIA), which employs contrastive learning with an instance classifier to align features between massive low- and few high-quality images. This approach effectively reduces the domain gap by bringing the latent representations of cross-quality images closer on an instance basis. Additionally, the system integrates an object image collection with a pre-trained deblurring model to enhance the observed image quality. Our method fine-tunes the SimSiam model, pre-trained on ImageNet, using CrossIA. We evaluated our method's effectiveness through an InstanceImageNav task with 20 different types of instances, where the robot identifies the same instance in a real-world environment as a high-quality query image. Our experiments showed that our method improves the task success rate by up to three times compared to the baseline, a conventional approach based on SuperGlue. These findings highlight the potential of leveraging contrastive learning and image enhancement techniques to bridge the domain gap and improve object localization in robotic applications. The project website is https://emergentsystemlabstudent.github.io/DomainBridgingNav/.

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arXiv2024

Object Instance Retrieval in Assistive Robotics: Leveraging Fine-Tuned SimSiam with Multi-View Images Based on 3D Semantic Map

Robots that assist humans in their daily lives should be able to locate specific instances of objects in an environment that match a user's desired objects. This task is known as instance-specific image goal navigation (InstanceImageNav), which requires a model that can distinguish different instances of an object within the same class. A significant challenge in robotics is that when a robot observes the same object from various 3D viewpoints, its appearance may differ significantly, making it difficult to recognize and locate accurately. In this paper, we introduce a method called SimView, which leverages multi-view images based on a 3D semantic map of an environment and self-supervised learning using SimSiam to train an instance-identification model on-site. The effectiveness of our approach was validated using a photorealistic simulator, Habitat Matterport 3D, created by scanning actual home environments. Our results demonstrate a 1.7-fold improvement in task accuracy compared with contrastive language-image pre-training (CLIP), a pre-trained multimodal contrastive learning method for object searching. This improvement highlights the benefits of our proposed fine-tuning method in enhancing the performance of assistive robots in InstanceImageNav tasks. The project website is https://emergentsystemlabstudent.github.io/MultiViewRetrieve/.

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arXiv2024

Preserving Real-World Finger Dexterity Using a Lightweight Fingertip Haptic Device for Virtual Dexterous Manipulation

This study presents a lightweight, wearable fingertip haptic device that provides physics-based haptic feedback for dexterous manipulation in virtual environments without hindering real-world interactions. The device's design utilizes thin strings and actuators attached to the fingernails, minimizing the weight (1.76g each finger) while preserving finger flexibility. Multiple types of haptic feedback are simulated by integrating the software with a physics engine. Experiments evaluate the device's performance in pressure perception, slip feedback, and typical dexterous manipulation tasks. and daily operations, while subjective assessments gather user experiences. Results demonstrate that participants can perceive and respond to pressure and vibration feedback. These limited haptic cues are crucial as they significantly enhance efficiency in virtual dexterous manipulation tasks. The device's ability to preserve tactile sensations and minimize hindrance to real-world operations is a key advantage over glove-type haptic devices. This research offers a potential solution for designing haptic interfaces that balance lightweight, haptic feedback for dexterous manipulation and daily wearability.

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arXiv2023

Integration of Independent Heat Transfer Mechanisms for Non-Contact Cold Sensation Presentation With Low Residual Heat

Thermal sensation is crucial to enhancing our comprehension of the world and enhancing our ability to interact with it. Therefore, the development of thermal sensation presentation technologies holds significant potential, providing a novel method of interaction. Traditional technologies often leave residual heat in the system or the skin, affecting subsequent presentations. Our study focuses on presenting thermal sensations with low residual heat, especially cold sensations. To mitigate the impact of residual heat in the presentation system, we opted for a non-contact method, and to address the influence of residual heat on the skin, we present thermal sensations without significantly altering skin temperature. Specifically, we integrated two highly responsive and independent heat transfer mechanisms: convection via cold air and radiation via visible light, providing non-contact thermal stimuli. By rapidly alternating between perceptible decreases and imperceptible increases in temperature on the same skin area, we maintained near-constant skin temperature while presenting continuous cold sensations. In our experiments involving 15 participants, we observed that when the cooling rate was -0.2 to -0.24 degree celsius per second and the cooling time ratio was 30 to 50 %, more than 86.67 % of the participants perceived only persistent cold without any warmth.

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arXiv2023

Navigation Method Enhancing Music Listening Experience by Stimulating Both Neck Sides with Modulated Music Vibration

We propose a method that stimulates musical vibration (generated from and synchronized with musical signals), modulated by the direction and distance to the target, on both sides of a user's neck with Hapbeat, a necklace-type haptic device.We conducted three experiments to confirm that the proposed method can achieve both haptic navigation and enhance the music-listening experience.Experiment 1 consisted of conducting a questionnaire survey to examine the effect of stimulating musical vibrations.Experiment 2 evaluated the accuracy (deg) of users' ability to adjust their direction toward a target using the proposed method.Experiment 3 examined the ability of four different navigation methods by performing navigation tasks in a virtual environment.The results of the experiments showed that stimulating musical vibration enhanced the music-listening experience, and that the proposed method is able to provide sufficient information to guide the users: accuracy in identifying directions was about 20 deg, participants reached the target in all navigation tasks, and in about 80% of all trials participants reached the target using the shortest route.Furthermore, the proposed method succeeded in conveying distance information, and Hapbeat can be combined with conventional navigation methods without interfering with music listening.

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