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Takayuki Nagai

Publications and source records attributed to Takayuki Nagai.

At least 19 recordsLinked to original sources
arXiv2026

Raman Optical Activity Induced by Ferroaxial Order in NiTiO$_3$

Raman optical activity (ROA), the dependence of Raman intensity on the circular polarization of incident and scattered light, has traditionally been observed in chiral molecules and magnetic materials, where inversion or time-reversal symmetry is broken. Here we demonstrate that ROA can also arise in a centrosymmetric and non-magnetic ferroaxial crystal. Using circularly polarized Raman spectroscopy on single-crystalline NiTiO$_3$, we observed a pronounced ROA signal in the cross-circular polarization configurations, which correlates with the ferroaxial domain structure. Our symmetry analysis, first-principles calculations of phonons, and tight-binding model calculations reveal that the natural ROA originates from the ferroaxial order and persists even within the electric dipole approximation. These results establish ROA as a powerful probe of ferroaxial order in centrosymmetric systems.

cond-mat.mtrl-sci↗
arXiv2025

Raman Spectroscopic Investigation of Ferroaxial Order in Na2BaNi(PO4)2 Single Crystals

Ferroaxial order is characterized by the breaking of mirror symmetry parallel to the crystallographic principal axis, which often originates from spontaneous rotational distortions of the crystal lattice. Such rotational distortions are, by symmetry, allowed to couple to specific phonon modes. However, Raman-active phonons associated with these rotational distortions have not yet been clearly identified on a symmetry-consistent basis. Here, we perform polarization-resolved Raman spectroscopy on the ferroaxial phase of Na2BaNi(PO4)2 single crystals and combine the measurements with first-principles lattice-dynamics calculations. This symmetry-guided analysis enables a comprehensive assignment of Raman-active modes in the ferroaxial phase. Several low-frequency Ag modes exhibit finite linewidth broadening, suggesting that these phonons may be weakly affected by the underlying rotational distortion. These results establish a symmetry-based spectroscopic framework for analyzing phonons associated with rotational distortions in ferroaxial materials and provide a basis for future studies of ferroaxial order in complex oxides.

cond-mat.mtrl-sci↗
arXiv2025

Nonreciprocal charge transport in an iron-based superconductor with broken inversion symmetry engineered by a hydrogen-concentration gradient

The breaking of spatial inversion symmetry in condensed matter gives rise to intriguing physical properties, such as ferroelectricity, piezoelectricity, spin-momentum locking, and nonreciprocal responses. Here we propose that a concentration gradient, which often persists as a quasi-stable nonequilibrium state with long relaxation times in solids, can serve as a general platform for inversion symmetry breaking. We demonstrate this concept in an epitaxial thin film of the hydrogen-doped SmFeAsO (Sm1111:H) superconductor with a depthwise hydrogen-concentration gradient introduced via an optimized topotactic reaction. This film exhibits nonreciprocal charge transport, meaning that the electrical resistance depends on the direction of the applied current, which serves as a key signature of broken inversion symmetry. A pronounced nonreciprocal signal emerges in the vicinity of the superconducting transition, which we attribute to vortex-motion nonreciprocity arising from an asymmetric pinning landscape created by the hydrogen-concentration gradient. Owing to the high critical temperature of Sm1111:H, vortex-origin nonreciprocity is observed above 40 K, representing the highest temperature reported to date among single bulk materials without an artificially hetero-layered structure. Our findings establish concentration-gradient engineering as a versatile and broadly applicable route for realizing inversion-broken states in otherwise centrosymmetric hosts, opening pathways toward a broader landscape of odd-parity-driven functionalities.

cond-mat.mtrl-sci↗
arXiv2024

Creative Agents: Simulating the Systems Model of Creativity with Generative Agents

With the growing popularity of generative AI for images, video, and music, we witnessed models rapidly improve in quality and performance. However, not much attention is paid towards enabling AI's ability to "be creative". In this study, we implemented and simulated the systems model of creativity (proposed by Csikszentmihalyi) using virtual agents utilizing large language models (LLMs) and text prompts. For comparison, the simulations were conducted with the "virtual artists" being: 1)isolated and 2)placed in a multi-agent system. Both scenarios were compared by analyzing the variations and overall "creativity" in the generated artifacts (measured via a user study and LLM). Our results suggest that the generative agents may perform better in the framework of the systems model of creativity.

cs.MA↗
arXiv2024

LiP-LLM: Integrating Linear Programming and dependency graph with Large Language Models for multi-robot task planning

This study proposes LiP-LLM: integrating linear programming and dependency graph with large language models (LLMs) for multi-robot task planning. In order for multiple robots to perform tasks more efficiently, it is necessary to manage the precedence dependencies between tasks. Although multi-robot decentralized and centralized task planners using LLMs have been proposed, none of these studies focus on precedence dependencies from the perspective of task efficiency or leverage traditional optimization methods. It addresses key challenges in managing dependencies between skills and optimizing task allocation. LiP-LLM consists of three steps: skill list generation and dependency graph generation by LLMs, and task allocation using linear programming. The LLMs are utilized to generate a comprehensive list of skills and to construct a dependency graph that maps the relationships and sequential constraints among these skills. To ensure the feasibility and efficiency of skill execution, the skill list is generated by calculated likelihood, and linear programming is used to optimally allocate tasks to each robot. Experimental evaluations in simulated environments demonstrate that this method outperforms existing task planners, achieving higher success rates and efficiency in executing complex, multi-robot tasks. The results indicate the potential of combining LLMs with optimization techniques to enhance the capabilities of multi-robot systems in executing coordinated tasks accurately and efficiently. In an environment with two robots, a maximum success rate difference of 0.82 is observed in the language instruction group with a change in the object name.

cs.RO↗
arXiv2024

Predictive Reachability for Embodiment Selection in Mobile Manipulation Behaviors

Mobile manipulators require coordinated control between navigation and manipulation to accomplish tasks. Typically, coordinated mobile manipulation behaviors have base navigation to approach the goal followed by arm manipulation to reach the desired pose. Selecting the embodiment between the base and arm can be determined based on reachability. Previous methods evaluate reachability by computing inverse kinematics and activate arm motions once solutions are identified. In this study, we introduce a new approach called predictive reachability that decides reachability based on predicted arm motions. Our model utilizes a hierarchical policy framework built upon a world model. The world model allows the prediction of future trajectories and the evaluation of reachability. The hierarchical policy selects the embodiment based on the predicted reachability and plans accordingly. Unlike methods that require prior knowledge about robots and environments for inverse kinematics, our method only relies on image-based observations. We evaluate our approach through basic reaching tasks across various environments. The results demonstrate that our method outperforms previous model-based approaches in both sample efficiency and performance, while enabling more reasonable embodiment selection based on predictive reachability.

cs.RO↗
arXiv2024

Goal Estimation-based Adaptive Shared Control for Brain-Machine Interfaces Remote Robot Navigation

In this study, we propose a shared control method for teleoperated mobile robots using brain-machine interfaces (BMI). The control commands generated through BMI for robot operation face issues of low input frequency, discreteness, and uncertainty due to noise. To address these challenges, our method estimates the user's intended goal from their commands and uses this goal to generate auxiliary commands through the autonomous system that are both at a higher input frequency and more continuous. Furthermore, by defining the confidence level of the estimation, we adaptively calculated the weights for combining user and autonomous commands, thus achieving shared control.

cs.RO↗
arXiv2024

Data-Efficient Approach to Humanoid Control via Fine-Tuning a Pre-Trained GPT on Action Data

There are several challenges in developing a model for multi-tasking humanoid control. Reinforcement learning and imitation learning approaches are quite popular in this domain. However, there is a trade-off between the two. Reinforcement learning is not the best option for training a humanoid to perform multiple behaviors due to training time and model size, and imitation learning using kinematics data alone is not appropriate to realize the actual physics of the motion. Training models to perform multiple complex tasks take long training time due to high DoF and complexities of the movements. Although training models offline would be beneficial, another issue is the size of the dataset, usually being quite large to encapsulate multiple movements. There are few implementations of transformer-based models to control humanoid characters and predict their motion based on a large dataset of recorded/reference motion. In this paper, we train a GPT on a large dataset of noisy expert policy rollout observations from a humanoid motion dataset as a pre-trained model and fine tune that model on a smaller dataset of noisy expert policy rollout observations and actions to autoregressively generate physically plausible motion trajectories. We show that it is possible to train a GPT-based foundation model on a smaller dataset in shorter training time to control a humanoid in a realistic physics environment to perform human-like movements.

cs.RO↗
arXiv2024

Proceedings of the Dialogue Robot Competition 2023

The Dialogic Robot Competition 2023 (DRC2023) is a competition for humanoid robots (android robots that closely resemble humans) to compete in interactive capabilities. This is the third year of the competition. The top four teams from the preliminary competition held in November 2023 will compete in the final competition on Saturday, December 23. The task for the interactive robots is to recommend a tourism plan for a specific region. The robots can employ multimodal behaviors, such as language and gestures, to engage the user in the sightseeing plan they recommend. In the preliminary round, the interactive robots were stationed in a travel agency office, where visitors conversed with them and rated their performance via a questionnaire. In the final round, dialogue researchers and tourism industry professionals interacted with the robots and evaluated their performance. This event allows visitors to gain insights into the types of dialogue services that future dialogue robots should offer. The proceedings include papers on dialogue systems developed by the 12 teams participating in DRC2023, as well as an overview of the papers provided by all the teams.

cs.RO↗
arXiv2023

Estimation of User's World Model Using Graph2vec

To obtain advanced interaction between autonomous robots and users, robots should be able to distinguish their state space representations (i.e., world models). Herein, a novel method was proposed for estimating the user's world model based on queries. In this method, the agent learns the distributed representation of world models using graph2vec and generates concept activation vectors that represent the meaning of queries in the latent space. Experimental results revealed that the proposed method can estimate the user's world model more efficiently than the simple method of using the ``AND'' search of queries.

cs.RO↗
arXiv2022

Overview of Dialogue Robot Competition 2022

Although many competitions have been held on dialogue systems in the past, no competition has been organized specifically for dialogue with humanoid robots. As the first such attempt in the world, we held a dialogue robot competition in 2020 to compare the performances of interactive robots using an android that closely resembles a human. Dialogue Robot Competition 2022 (DRC2022) was the second competition, held in August 2022. The task and regulations followed those of the first competition, while the evaluation method was improved and the event was internationalized. The competition has two rounds, a preliminary round and the final round. In the preliminary round, twelve participating teams competed in performance of a dialogue robot in the manner of a field experiment, and then three of those teams were selected as finalists. The final round will be held on October 25, 2022, in the Robot Competition session of IROS2022. This paper provides an overview of the task settings and evaluation method of DRC2022 and the results of the preliminary round.

cs.RO↗
arXiv2022

Fully automated spectroscopic ellipsometry analyses of crystalline-phase semiconductors based on a new algorithm

One significant drawback of a spectroscopic ellipsometry (SE) technique is its time-consuming and often complicated analysis procedure necessary to assess the optical functions of thin-film and bulk samples. Here, to solve this inherent problem of a traditional SE method, we present a new general way that allows full automation of SE analyses for crystalline-phase semiconductors exhibiting complex absorption features. In particular, we have modified a scheme established in our previous study, which performs a non-linear SE fitting analysis only in a low energy region at the beginning, while the analyzed energy region is gradually expanded toward higher energy by incorporating addition optical transition peaks. In this study, we have further developed a unique analyzing-energy search algorithm, in which a proper analyzing-energy region is determined to incorporate the feature of a new transition peak. In the developed method, a drastic improvement over the previous simple approach has been confirmed for expressing complex dielectric functions consisting of sharp and broad absorption peaks. The proposed method (Delta M method) has been applied successfully to analyze perovskite-based crystalline samples, including hybrid perovskite (CH3NH3PbI3) and chalcogenide perovskites (SrHfS3 and BaZrS3). In the automated analyses of these semiconductors, 7-8 transition peaks are introduced automatically to describe sample dielectric functions, while structural parameters, such as thin-film and roughness thicknesses, are also determined simultaneously. The established method can drastically reduce an analysis time to a level that allows the automatic inspection of daily varying material optical properties and expands the application area of spectroscopic ellipsometry considerably.

cond-mat.mtrl-sci↗
arXiv2022

Survey and Perspective on Social Emotions in Robotics

This study reviews research on social emotions in robotics. In robotics, the study of emotions has been pursued for a long time, including the study of their recognition, expression, and computational modeling of the basic mechanisms which underlie them. Research has advanced according to well-known psychological findings, such as category and dimension theories. Many studies have been based on these basic theories, addressing only basic emotions. However, social emotions, also referred to as higher-level emotions, have been studied in psychology. We believe that these higher-level emotions are worth pursuing in robotics for next-generation, socially aware robots. In this review paper, we summarize the findings on social emotions in psychology and neuroscience, along with a survey of the studies on social emotions in robotics that have been conducted to date. Thereafter, research directions toward the implementation of social emotions in robots are discussed.

cs.RO↗
arXiv2022

A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots

Building a humanlike integrative artificial cognitive system, that is, an artificial general intelligence (AGI), is the holy grail of the artificial intelligence (AI) field. Furthermore, a computational model that enables an artificial system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes an approach to develop a cognitive architecture by integrating elemental cognitive modules to enable the training of the modules as a whole. This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model(PGM)-based cognitive system to develop a cognitive system for developmental robots by integrating PGMs. The development framework is called a whole brain PGM (WB-PGM), which differs fundamentally from existing cognitive architectures in that it can learn continuously through a system based on sensory-motor information. In this study, we describe the rationale of WB-PGM, the current status of PGM-based elemental cognitive modules, their relationship with the human brain, the approach to the integration of the cognitive modules, and future challenges. Our findings can serve as a reference for brain studies. As PGMs describe explicit informational relationships between variables, this description provides interpretable guidance from computational sciences to brain science. By providing such information, researchers in neuroscience can provide feedback to researchers in AI and robotics on what the current models lack with reference to the brain. Further, it can facilitate collaboration among researchers in neuro-cognitive sciences as well as AI and robotics.

cs.AI↗
arXiv2021

Explainable Autonomous Robots: A Survey and Perspective

Advanced communication protocols are critical to enable the coexistence of autonomous robots with humans. Thus, the development of explanatory capabilities is an urgent first step toward autonomous robots. This survey provides an overview of the various types of "explainability" discussed in machine learning research. Then, we discuss the definition of "explainability" in the context of autonomous robots (i.e., explainable autonomous robots) by exploring the question "what is an explanation?" We further conduct a research survey based on this definition and present some relevant topics for future research.

cs.AI↗
arXiv2021

A Framework of Explanation Generation toward Reliable Autonomous Robots

To realize autonomous collaborative robots, it is important to increase the trust that users have in them. Toward this goal, this paper proposes an algorithm which endows an autonomous agent with the ability to explain the transition from the current state to the target state in a Markov decision process (MDP). According to cognitive science, to generate an explanation that is acceptable to humans, it is important to present the minimum information necessary to sufficiently understand an event. To meet this requirement, this study proposes a framework for identifying important elements in the decision-making process using a prediction model for the world and generating explanations based on these elements. To verify the ability of the proposed method to generate explanations, we conducted an experiment using a grid environment. It was inferred from the result of a simulation experiment that the explanation generated using the proposed method was composed of the minimum elements important for understanding the transition from the current state to the target state. Furthermore, subject experiments showed that the generated explanation was a good summary of the process of state transition, and that a high evaluation was obtained for the explanation of the reason for an action.

cs.AI↗
arXiv2020

lamBERT: Language and Action Learning Using Multimodal BERT

Recently, the bidirectional encoder representations from transformers (BERT) model has attracted much attention in the field of natural language processing, owing to its high performance in language understanding-related tasks. The BERT model learns language representation that can be adapted to various tasks via pre-training using a large corpus in an unsupervised manner. This study proposes the language and action learning using multimodal BERT (lamBERT) model that enables the learning of language and actions by 1) extending the BERT model to multimodal representation and 2) integrating it with reinforcement learning. To verify the proposed model, an experiment is conducted in a grid environment that requires language understanding for the agent to act properly. As a result, the lamBERT model obtained higher rewards in multitask settings and transfer settings when compared to other models, such as the convolutional neural network-based model and the lamBERT model without pre-training.

cs.LG↗