Author name cluster

Kohei Ogawa

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

1 author row

IROS Conference 2025 Conference Paper

Anomaly Detection in Human-Robot Interaction Using Multimodal Models Constructed from In-the-Wild Interactions

Shota Mochizuki
Sanae Yamashita
Kenya Hoshimure
Jun Baba
Tomonori Kubota
Kohei Ogawa
Ryuichiro Higashinaka

In recent years, numerous studies have been conducted on dialogue robots powered by large language models, enabling sophisticated interactions such as providing guidance and engaging in small talk. However, the interaction performance remains imperfect, and the robots sometimes cause problems during interactions. In this study, we aim to automatically detect such anomalies in human-robot interactions by creating a dataset and developing anomaly detection models. To this end, we created a dataset by manually annotating videos of in-the-wild interactions collected from our field experiment designed to test a framework of parallel conversations in which a human intervenes when a problem occurs in the interaction. Using this dataset, we trained classification models to construct anomaly detection models. We then conducted another field experiment in which the model’s detection results were presented as alerts to operators within the parallel conversation framework. The results confirmed that providing alerts on the basis of the anomaly detection model was useful for facilitating operator intervention.

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IROS Conference 2020 Conference Paper

Smart Speaker vs. Social Robot in a Case of Hotel Room

Junya Nakanishi
Jun Baba
Itaru Kuramoto
Kohei Ogawa
Yuichiro Yoshikawa
Hiroshi Ishiguro

Under the circumstances that social robots are increasingly being developed and studied in service encounters at public spaces, are they introduced into residential environments (i. e. , private space)? This study hypothesizes that a personal assistant device in residential environments should wear human-like appearance to engage in service as conversation partner. We implemented the interaction design that provides regular services as the current personal assistant and additional service as conversation partner, and then conducted a field experiment where the participants stayed in the hotel rooms with a smart speaker or a social robot. The results support the hypothesis in that of conversation amount and emotional experience by conversation. The results also suggest the possibility of commercial service, namely conversational advertisement through social robots.

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IROS Conference 2018 Conference Paper

Excuse Me, May I Say Something? A Robot Facilitating Q&A for Lectures

Oskar Palinko
Jiro Shimaya
Kristian Hoeck
Kohei Ogawa
Nobuhiro Jinnai
Yuichiro Yoshikawa
Hiroshi Ishiguro

Hiroshi Ishiguro gave a lecture to a group of young students. We employed CommU, the desktop social robot, to manage the questions and answers for the talk. We encouraged the students to ask questions anytime. Half of the classroom was told to ask questions by raising their hand while the other half was shown an online messaging system developed for CommU, which allows the audience to post questions, which the robot would directly say. We had a gatekeeper to monitor for invalid sentences. In the middle of the presentation we asked the students to shift roles. The robot used a neural network based estimator of interruptibility to find the best time to speak. We did not expect too many questions, but the audience really embraced using the robot. They posted 44 questions to the presenter through CommU. On the other hand they asked 8 direct questions by raising their hands and standing up. Students thought that they gained more information from the lecturer using the robot than using the conventional method. In this instance we didn't stop the students from asking too many questions, but in a real-world application the gatekeeper will have to play an important role.

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ICML Conference 2014 Conference Paper

Outlier Path: A Homotopy Algorithm for Robust SVM

Shinya Suzumura
Kohei Ogawa
Masashi Sugiyama
Ichiro Takeuchi

In recent applications with massive but less reliable data (e. g. , labels obtained by a semi-supervised learning method or crowdsourcing), non-robustness of the support vector machine (SVM) often causes considerable performance deterioration. Although improving the robustness of SVM has been investigated for long time, robust SVM (RSVM) learning still poses two major challenges: obtaining a good (local) solution from a non-convex optimization problem and optimally controlling the robustness-efficiency trade-off. In this paper, we address these two issues simultaneously in an integrated way by introducing a novel homotopy approach to RSVM learning. Based on theoretical investigation of the geometry of RSVM solutions, we show that a path of local RSVM solutions can be computed efficiently when the influence of outliers is gradually suppressed as simulated annealing. We experimentally demonstrate that our algorithm tends to produce better local solutions than the alternative approach based on the concave-convex procedure, with the ability of stable and efficient model selection for controlling the influence of outliers.

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ICML Conference 2013 Conference Paper

Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines

Kohei Ogawa
Motoki Imamura
Ichiro Takeuchi
Masashi Sugiyama

The semi-supervised support vector machine (S3VM) is a maximum-margin classification algorithm based on both labeled and unlabeled data. Training S3VM involves either a combinatorial or non-convex optimization problem and thus finding the global optimal solution is intractable in practice. It has been demonstrated that a key to successfully find a good (local) solution of S3VM is to gradually increase the effect of unlabeled data, a la annealing. However, existing algorithms suffer from the trade-off between the resolution of annealing steps and the computation cost. In this paper, we go beyond this trade-off by proposing a novel training algorithm that efficiently performs annealing with an infinitesimal resolution. Through experiments, we demonstrate that the proposed infinitesimal annealing algorithm tends to produce better solutions with less computation time than existing approaches.

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ICML Conference 2013 Conference Paper

Safe Screening of Non-Support Vectors in Pathwise SVM Computation

Kohei Ogawa
Yoshiki Suzuki
Ichiro Takeuchi

In this paper, we claim that some of the non-support vectors (non-SVs) that have no influence on the classifier can be screened out prior to the training phase in pathwise SVM computation scenario, in which one is asked to train a sequence (or path) of SVM classifiers for different regularization parameters. Based on a recently proposed framework so-called safe screening rule, we derive a rule for screening out non-SVs in advance, and discuss how we can exploit the advantage of the rule in pathwise SVM computation scenario. Experiments indicate that our approach often substantially reduce the total pathwise computation cost.

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