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Ryosuke Kojima

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9 papers

2 author rows

AAAI Conference 2025 Conference Paper

Learning Deep Dissipative Dynamics

Yuji Okamoto
Ryosuke Kojima

This study challenges strictly guaranteeing ``dissipativity'' of a dynamical system represented by neural networks learned from given time-series data. Dissipativity is a crucial indicator for dynamical systems that generalizes stability and input-output stability, known to be valid across various systems including robotics, biological systems, and molecular dynamics. By analytically proving the general solution to the nonlinear Kalman–Yakubovich–Popov (KYP) lemma, which is the necessary and sufficient condition for dissipativity, we propose a differentiable projection that transforms any dynamics represented by neural networks into dissipative ones and a learning method for the transformed dynamics. Utilizing the generality of dissipativity, our method strictly guarantee stability, input-output stability, and energy conservation of trained dynamical systems. Finally, we demonstrate the robustness of our method against out-of-domain input through applications to robotic arms and fluid dynamics.

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NeurIPS Conference 2022 Conference Paper

Learning Deep Input-Output Stable Dynamics

Ryosuke Kojima
Yuji Okamoto

Learning stable dynamics from observed time-series data is an essential problem in robotics, physical modeling, and systems biology. Many of these dynamics are represented as an inputs-output system to communicate with the external environment. In this study, we focus on input-output stable systems, exhibiting robustness against unexpected stimuli and noise. We propose a method to learn nonlinear systems guaranteeing the input-output stability. Our proposed method utilizes the differentiable projection onto the space satisfying the Hamilton-Jacobi inequality to realize the input-output stability. The problem of finding this projection can be formulated as a quadratic constraint quadratic programming problem, and we derive the particular solution analytically. Also, we apply our method to a toy bistable model and the task of training a benchmark generated from a glucose-insulin simulator. The results show that the nonlinear system with neural networks by our method achieves the input-output stability, unlike naive neural networks. Our code is available at https: //github. com/clinfo/DeepIOStability.

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KR Conference 2021 Conference Paper

Boolean Network Learning in Vector Spaces for Genome-wide Network Analysis

Taisuke Sato
Ryosuke Kojima

Boolean networks (BNs) are one of the standard tools for modeling gene regulatory networks in biology but their learning has been limited to small networks due to computational difficulty. Aiming at unprecedented scalability, we focus on a subclass of BNs called AND/OR Boolean networks where Boolean formulas are restricted to a conjunction or a disjunction of literals. We represent an AND/OR BN with N nodes by an N x 2N binary matrix Q paired with an N dimensional integer vector theta called a threshold vector, a state of the BN by an N dimensional binary state vector s and a state transition by matrix operations on Q, theta and s. Given a list of state transitions S = s_0. .. s_L, we learn Q and theta in a continuous space by minimizing a cost function J(Q*, theta, S) w. r. t. a real number matrix Q* and theta while thresholding Q* into a binary matrix Q using theta so that Q represents an AND/OR BN realizing the target state transitions S. We conducted experiments with artificial and real data sets to check scalability and accuracy of our learning algorithm. First we randomly generated AND/OR BNs up to N=5, 000 nodes and empirically confirmed O(N^2) learning time behavior using them. We also observed 99. 8% bit-by-bit prediction accuracy (prediction accuracy = 1 - test error) with state transition data generated by AND/OR BNs. For real data, we learned genome-wide AND/OR BNs with 10, 928 nodes for budding yeast from transcription profiling data sets, each containing 10, 928 mRNAs and 40 transitions and achieved for instance 84. 3% prediction accuracy and successfully extracted more than 6, 000 small AND/ORs whose average prediction accuracy reaches much higher 94. 9%.

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NeSy Conference 2019 Conference Paper

T-PRISM: A tensorized logic programming language for data modelling

Ryosuke Kojima
Taisuke Sato

We introduce a new logic programming language T-PRISM based on tensor embeddings. Our embedding scheme is a modification of the distribution semantics in PRISM, one of the state-of-the-art probabilistic logic programming languages, by replacing distribution functions with multidimensional arrays, i. e. , tensors. T-PRISM consists of two parts: logic programming part and numerical computation part. The former provides flexible and interpretable modeling at the level of first order logic, and the latter part provides scalable computation utilizing parallelization and hardware acceleration with GPUs. Combing these two parts provides a remarkably wide range of high-level declarative modeling from symbolic reasoning to deep learning. To embody this programming language, we also introduce a new semantics, termed tensorized semantics, which combines the traditional least model semantics in logic programming with the embeddings of tensors. In T-PRISM, we first derive a set of equations related to tensors from a given program using logical inference, i. e. , Prolog execution in a symbolic space and then solve the derived equations in a continuous space by TensorFlow. Using our preliminary implementation of T-PRISM, we have successfully dealt with a wide range of modeling. We have succeeded in dealing with real large-scale data in the declarative modeling. This paper presents a DistMult model for knowledge graphs using the FB15k and WN18 datasets.

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IROS Conference 2018 Conference Paper

HARK-Bird-Box: A Portable Real-time Bird Song Scene Analysis System

Ryosuke Kojima
Osamu Sugiyama
Kotaro Hoshiba
Reiji Suzuki
Kazuhiro Nakadai

This paper addresses real-time bird song scene analysis. Observation of animal behavior such as communication of wild birds would be aided by a portable device implementing a real-time system that can localize sound sources, measure their timing, classify their sources, and visualize these factors of sources. The difficulty of such a system is an integration of these functions considering the real-time requirement. To realize such a system, we propose a cascaded approach, cascading sound source detection, localization, separation, feature extraction, classification, and visualization for bird song analysis. Our system is constructed by combining an open source software for robot audition called HARK and a deep learning library to implement a bird song classifier based on a convolutional neural network (CNN). Considering portability, we implemented this system on a single-board computer, Jetson TX2, with a microphone array and developed a prototype device for bird song scene analysis. A preliminary experiment confirms a computational time for the whole system to realize a real-time system. Also, an additional experiment with a bird song dataset revealed a trade-off relationship between classification accuracy and time consuming and the effectiveness of our classifier.

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IROS Conference 2017 Conference Paper

Development of microphone-array-embedded UAV for search and rescue task

Kazuhiro Nakadai
Makoto Kumon
Hiroshi G. Okuno
Kotaro Hoshiba
Mizuho Wakabayashi
Kai Washizaki
Takahiro Ishiki
Daniel Gabriel

This paper addresses online outdoor sound source localization using a microphone array embedded in an unmanned aerial vehicle (UAV). In addition to sound source localization, sound source enhancement and robust communication method are also described. This system is one instance of deployment of our continuously developing open source software for robot audition called HARK (Honda Research Institute Japan Audition for Robots with Kyoto University). To improve the robustness against outdoor acoustic noise, we propose to combine two sound source localization methods based on MUSIC (multiple signal classification) to cope with trade-off between latency and noise robustness. The standard Eigenvalue decomposition based MUSIC (SEVD-MUSIC) has smaller latency but less noise robustness, whereas the incremental generalized singular value decomposition based MUSIC (iGSVD-MUSIC) has higher noise robustness but larger latency. A UAV operator can use an appropriate method according to the situation. A sound enhancement method called online robust principal component analysis (ORPCA) enables the operator to detect a target sound source more easily. To improve the stability of wireless communication, and robustness of the UAV system against weather changes, we developed data compression based on free lossless audio codec (FLAC) extended to support a 16 ch audio data stream via UDP, and developed a water-resistant microphone array. The resulting system successfully worked in an outdoor search and rescue task in ImPACT Tough Robotics Challenge in November 2016.

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IROS Conference 2016 Conference Paper

Partially Shared Deep Neural Network in sound source separation and identification using a UAV-embedded microphone array

Takayuki Morito
Osamu Sugiyama
Ryosuke Kojima
Kazuhiro Nakadai

This paper addresses sound source separation and identification for noise-contaminated acoustic signals recorded with a microphone array embedded in an Unmanned Aerial Vehicle (UAV), aiming at people's voice detection quickly and widely in a disaster situation. The key approach to achieve this is Deep Neural Network (DNN), but it is well known that training a DNN needs a huge dataset to improve its performance. In a practical application, building such a dataset is not often realistic owing to the cost of manual data annotation. Therefore, we propose a Partially-Shared Deep Neural Network (PS-DNN) which can learn multiple tasks at the same time with a small amount of annotated data. Preliminary results show that the PS-DNN outperforms conventional DNN-based approaches which require fully-annotated data in training in terms of identification accuracy. In addition, it maintains performance even when noise-suppressed signals are used for sound source separation training, and partially annotated data is used for sound source identification training.

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IROS Conference 2016 Conference Paper

Semi-automatic bird song analysis by spatial-cue-based integration of sound source detection, localization, separation, and identification

Ryosuke Kojima
Osamu Sugiyama
Reiji Suzuki
Kazuhiro Nakadai
Charles E. Taylor

This paper addresses bird song analysis based on semi-automatic annotation. Research in animal behavior, especially with birds, would be aided by automated (or semiautomated) systems that can localize sounds, measure their timing, and identify their source. This is difficult to achieve in real environments where several birds may be singing from different locations and at the same time. Analysis of recordings from the wild has in the past typically required manual annotation. Such annotation is not always accurate or even consistent, as it may vary both within or between observers. Here we propose a system that uses automated methods from robot audition, including sound source detection, localization, separation and identification. In robot audition these technologies have typically been studied separately; combining them often leads to poor performance in real-time application from the wild. We suggest that integration is aided by placing a primary focus on spatial cues, then combining other features within a Bayesian framework. A second problem has been that supervised machine learning methods typically requires a pre-trained model that may require a large training set of annotated labels. We have employed a semi-automatic annotation approach that requires much less pre-annotation. Preliminary experiments with recordings of bird songs from the wild revealed that for identification accuracy our system outperformed a method based on conventional robot audition.

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IROS Conference 2015 Conference Paper

Audio-visual scene understanding utilizing text information for a cooking support robot

Ryosuke Kojima
Osamu Sugiyama
Kazuhiro Nakadai

This paper addresses multimodal “scene understanding” for a robot using audio-visual and text information. Scene understanding is defined by extracting six-W information such as What, When, Where, Who, Why, and hoW on the surrounding environment. Although scene understanding for a robot has been studied in the fields of robot vision and audition, only the first four Ws except for why and how information were considered. We, thus, focus on extracting how information, in particular, on cooking scenes. In cooking scenes, we define how information as a cooking procedure, and it is useful that a robot gives appropriate advice for cooking. To realize such cooking support, we propose a multi-modal cooking procedure recognition framework consisting of Convolutional Neural Network (CNN), and Hierarchical Hidden Markov Model (HHMM). CNN is knows as one of the most advanced classifiers, and it is applied to recognize a cooking events from audio and visual information. HHMM models a cooking procedure represented by a sequence of cooking events, which is defined as a relationship between cooking events using text data obtained from web, and the cooking events classified with CNN. Therefore, our proposed framework integrates these three types of modalities. We constructed an interactive cooking support system based on the proposed framework, which advice a next step in the current cooking procedure through human-robot communication. Preliminary results with simulated and real recorded multi-modal scenes showed the robustness of the proposed framework in a noisy and/or occluded situation.

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