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This paper proposed a straightforward and efficient current control solution for induction machines employing deep symbolic regression (DSR). The proposed DSR-based control design offers a simple yet highly effective approach by creating an optimal control model through training and fitting, resulting in an analytical dynamic numerical expression that characterizes the data. Notably, this approach not only produces an understandable model but also demonstrates the capacity to extrapolate and estimate data points outside its training dataset, showcasing its adaptability and resilience. In contrast to conventional state-of-the-art proportional-integral (PI) current controllers, which heavily rely on specific system models, the proposed DSR-based approach stands out for its model independence. Simulation and experimental tests validate its effectiveness, highlighting its superior extrapolation capabilities compared to conventional methods. These findings pave the way for the integration of deep learning methods in power conversion applications, promising improved performance and adaptability in the control of induction machines. The simulation and experimental test results are provided with a 3.7 kw induction machine to verify the efficacy of the proposed control solution., Comment: This work has been accepted for potential publication at the IEEE ECCE Asia 2024 International Power Electronics and Motion Control Conference. Please note that copyright may be transferred without prior notice

In restaurants, many aspects of customer service, such as greeting customers, taking orders, and processing payments, are automated. Due to the various cuisines, required services, and different standards of each restaurant, one challenging part of making the entire automated process is inspecting and providing appropriate services at the table during a meal. In this paper, we demonstrate an approach for automatically checking and providing services at the table. We initially construct a base model to recognize common information to comprehend the context of the table, such as object category, remaining food quantity, and meal progress status. After that, we add a service recognition classifier and retrain the model using a small amount of local restaurant data. We gathered data capturing the restaurant table during the meal in order to find a suitable service recognition classifier. With different inputs, combinations, time series, and data choices, we carried out a variety of tests. Through these tests, we discovered that the model with few significant data points and trainable parameters is more crucial in the case of sparse and redundant retraining data., Comment: 4 pages, 4 figures

Large language models (LLMs) have recently received considerable attention as alternative solutions for task planning. However, comparing the performance of language-oriented task planners becomes difficult, and there exists a dearth of detailed exploration regarding the effects of various factors such as pre-trained model selection and prompt construction. To address this, we propose a benchmark system for automatically quantifying performance of task planning for home-service embodied agents. Task planners are tested on two pairs of datasets and simulators: 1) ALFRED and AI2-THOR, 2) an extension of Watch-And-Help and VirtualHome. Using the proposed benchmark system, we perform extensive experiments with LLMs and prompts, and explore several enhancements of the baseline planner. We expect that the proposed benchmark tool would accelerate the development of language-oriented task planners., Comment: ICLR 2024. Code: https://github.com/lbaa2022/LLMTaskPlanning

Shared autonomy imitation learning, in which robots share workspace with humans for learning, enables correct actions in unvisited states and the effective resolution of compounding errors through expert's corrections. However, it demands continuous human attention and supervision to lead the demonstrations, without considering the risks associated with human judgment errors and delayed interventions. This can potentially lead to high levels of fatigue for the demonstrator and the additional errors. In this work, we propose an uncertainty-aware shared autonomy system that enables the robot to infer conservative task skills considering environmental uncertainties and learning from expert demonstrations and corrections. To enhance generalization and scalability, we introduce a hierarchical structure-based skill uncertainty inference framework operating at more abstract levels. We apply this to robot motion to promote a more stable interaction. Although shared autonomy systems have demonstrated high-level results in recent research and play a critical role, specific system design details have remained elusive. This paper provides a detailed design proposal for a shared autonomy system considering various robot configurations. Furthermore, we experimentally demonstrate the system's capability to learn operational skills, even in dynamic environments with interference, through pouring and pick-and-place tasks. Our code will be released soon., Comment: Submitted to ICRA 2024 and currently under review

Culture is a collection of connected and potentially interactive patterns that characterize a social group or a passed-on idea that people acquire as members of society. While offline activities can provide a better picture of the geographical association of cultural traits than online activities, gathering such data on a large scale has been challenging. Here, we use multi-decade longitudinal records of cultural events from Meetup.com, the largest event-based social networking service, to examine the landscape of offline cultural events. We analyze the temporal and categorical event dynamics driven by cultural diversity using over 2 million event logs collected over 17 years in 90 countries. Our results show that the national economic status explains 44.6 percent of the variance in total event count, while cultural characteristics such as individualism and long-term orientation explain 32.8 percent of the variance in topic categories. Furthermore, our analysis using hierarchical clustering reveals cultural proximity between the topics of socio-cultural activities (e.g., politics, leisure, health, technology). We expect that this work provides a landscape of social and cultural activities across the world, which allows us to better understand their dynamical patterns as well as their associations with cultural characteristics.

To provide effective and enjoyable human-robot interaction, it is important for social robots to exhibit nonverbal behaviors, such as a handshake or a hug. However, the traditional approach of reproducing pre-coded motions allows users to easily predict the reaction of the robot, giving the impression that the robot is a machine rather than a real agent. Therefore, we propose a neural network architecture based on the Seq2Seq model that learns social behaviors from human-human interactions in an end-to-end manner. We adopted a generative adversarial network to prevent invalid pose sequences from occurring when generating long-term behavior. To verify the proposed method, experiments were performed using the humanoid robot Pepper in a simulated environment. Because it is difficult to determine success or failure in social behavior generation, we propose new metrics to calculate the difference between the generated behavior and the ground-truth behavior. We used these metrics to show how different network architectural choices affect the performance of behavior generation, and we compared the performance of learning multiple behaviors and that of learning a single behavior. We expect that our proposed method can be used not only with home service robots, but also for guide robots, delivery robots, educational robots, and virtual robots, enabling the users to enjoy and effectively interact with the robots., Comment: 10 pages, 7 figures, 3 tables, submitted to the International Journal of Robotics Research (IJRR)

Non-verbal behavior is essential for embodied agents like social robots, virtual avatars, and digital humans. Existing behavior authoring approaches including keyframe animation and motion capture are too expensive to use when there are numerous utterances requiring gestures. Automatic generation methods show promising results, but their output quality is not satisfactory yet, and it is hard to modify outputs as a gesture designer wants. We introduce a new gesture generation toolkit, named SGToolkit, which gives a higher quality output than automatic methods and is more efficient than manual authoring. For the toolkit, we propose a neural generative model that synthesizes gestures from speech and accommodates fine-level pose controls and coarse-level style controls from users. The user study with 24 participants showed that the toolkit is favorable over manual authoring, and the generated gestures were also human-like and appropriate to input speech. The SGToolkit is platform agnostic, and the code is available at https://github.com/ai4r/SGToolkit., Comment: Accepted to UIST'21

This paper introduces a large-scale Korean speech dataset, called VOTE400, that can be used for analyzing and recognizing voices of the elderly people. The dataset includes about 300 hours of continuous dialog speech and 100 hours of read speech, both recorded by the elderly people aged 65 years or over. A preliminary experiment showed that speech recognition system trained with VOTE400 can outperform conventional systems in speech recognition of elderly people's voice. This work is a multi-organizational effort led by ETRI and MINDs Lab Inc. for the purpose of advancing the speech recognition performance of the elderly-care robots., Comment: 3 pages, 7 tables

For human-like agents, including virtual avatars and social robots, making proper gestures while speaking is crucial in human--agent interaction. Co-speech gestures enhance interaction experiences and make the agents look alive. However, it is difficult to generate human-like gestures due to the lack of understanding of how people gesture. Data-driven approaches attempt to learn gesticulation skills from human demonstrations, but the ambiguous and individual nature of gestures hinders learning. In this paper, we present an automatic gesture generation model that uses the multimodal context of speech text, audio, and speaker identity to reliably generate gestures. By incorporating a multimodal context and an adversarial training scheme, the proposed model outputs gestures that are human-like and that match with speech content and rhythm. We also introduce a new quantitative evaluation metric for gesture generation models. Experiments with the introduced metric and subjective human evaluation showed that the proposed gesture generation model is better than existing end-to-end generation models. We further confirm that our model is able to work with synthesized audio in a scenario where contexts are constrained, and show that different gesture styles can be generated for the same speech by specifying different speaker identities in the style embedding space that is learned from videos of various speakers. All the code and data is available at https://github.com/ai4r/Gesture-Generation-from-Trimodal-Context., Comment: 16 pages; ACM Transactions on Graphics (SIGGRAPH Asia 2020)

To better interact with users, a social robot should understand the users' behavior, infer the intention, and respond appropriately. Machine learning is one way of implementing robot intelligence. It provides the ability to automatically learn and improve from experience instead of explicitly telling the robot what to do. Social skills can also be learned through watching human-human interaction videos. However, human-human interaction datasets are relatively scarce to learn interactions that occur in various situations. Moreover, we aim to use service robots in the elderly-care domain; however, there has been no interaction dataset collected for this domain. For this reason, we introduce a human-human interaction dataset for teaching non-verbal social behaviors to robots. It is the only interaction dataset that elderly people have participated in as performers. We recruited 100 elderly people and two college students to perform 10 interactions in an indoor environment. The entire dataset has 5,000 interaction samples, each of which contains depth maps, body indexes and 3D skeletal data that are captured with three Microsoft Kinect v2 cameras. In addition, we provide the joint angles of a humanoid NAO robot which are converted from the human behavior that robots need to learn. The dataset and useful python scripts are available for download at https://github.com/ai4r/AIR-Act2Act. It can be used to not only teach social skills to robots but also benchmark action recognition algorithms., Comment: 6 pages, 6 figures, 2 tables, submitted to the International Journal of Robotics Research (IJRR)

A comparative analysis of vector control scheme based on different current control switching pulses (HC, SPWM, DPWM and SVPWM) for the speed response of motor drive is analysed in this paper. The control system using different switching techniques, are comparatively simulated and analysed. Ac motor drives are progressively used in high-performance application industries due to small size, efficient performance, robust to torque response and high power to size ratio. A mathematical model of ac motor drives is presented in order to explain the numerical theory of motor drives. The vector control technique is utilized for efficient speed control of ac motor drive based on independent torque and air gap flux control. The study compares the total harmonic distortion contents of phase currents of ac motor drive and speed response in each case. The simulation result shows that total harmonic distortion across the phase current in SVPWM is less as compared to other switching techniques while the rise time in speed response across SVPWM technique is faster as compared to other switching methods. The simulation result of ac motor drives speed control is demonstrated in Matlab/Simulink 2018b.

Deep learning, based on which many modern algorithms operate, is well known to be data-hungry. In particular, the datasets appropriate for the intended application are difficult to obtain. To cope with this situation, we introduce a new dataset called ETRI-Activity3D, focusing on the daily activities of the elderly in robot-view. The major characteristics of the new dataset are as follows: 1) practical action categories that are selected from the close observation of the daily lives of the elderly; 2) realistic data collection, which reflects the robot's working environment and service situations; and 3) a large-scale dataset that overcomes the limitations of the current 3D activity analysis benchmark datasets. The proposed dataset contains 112,620 samples including RGB videos, depth maps, and skeleton sequences. During the data acquisition, 100 subjects were asked to perform 55 daily activities. Additionally, we propose a novel network called four-stream adaptive CNN (FSA-CNN). The proposed FSA-CNN has three main properties: robustness to spatio-temporal variations, input-adaptive activation function, and extension of the conventional two-stream approach. In the experiment section, we confirmed the superiority of the proposed FSA-CNN using NTU RGB+D and ETRI-Activity3D. Further, the domain difference between both groups of age was verified experimentally. Finally, the extension of FSA-CNN to deal with the multimodal data was investigated.

Training an object instance detector where only a few training object images are available is a challenging task. One solution is a cut-and-paste method that generates a training dataset by cutting object areas out of training images and pasting them onto other background images. A detector trained on a dataset generated with a cut-and-paste method suffers from the conventional domain shift problem, which stems from a discrepancy between the source domain (generated training dataset) and the target domain (real test dataset). Though state-of-the-art domain adaptation methods are able to reduce this gap, it is limited because they do not consider the difference of domain gaps of foreground and background. In this study, we present that the conventional domain gap can be divided into two sub-domain gaps for foreground and background. Then, we show that the original cut-and-paste approach suffers from a new domain gap problem, an unbalanced domain gaps, because it has two separate source domains for foreground and background, unlike the conventional domain shift problem. Then, we introduce an advanced cut-and-paste method to balance the unbalanced domain gaps by diversifying the foreground with GAN (generative adversarial network)-generated seed images and simplifying the background using image processing techniques. Experimental results show that our method is effective for balancing domain gaps and improving the accuracy of object instance detection in a cluttered indoor environment using only a few seed images. Furthermore, we show that balancing domain gaps can improve the detection accuracy of state-of-the-art domain adaptation methods., Comment: 12 pages, 7 figures

This work presents an adaptive activation method for neural networks that exploits the interdependency of features. Each pixel, node, and layer is assigned with a polynomial activation function, whose coefficients are provided by an auxiliary activation network. The activation of a feature depends on the features of neighboring pixels in a convolutional layer and other nodes in a dense layer. The dependency is learned from data by the activation networks. In our experiments, networks with activation networks provide significant performance improvement compared to the baseline networks on which they are built. The proposed method can be used to improve the network performance as an alternative to increasing the number of nodes and layers.

Co-speech gestures enhance interaction experiences between humans as well as between humans and robots. Existing robots use rule-based speech-gesture association, but this requires human labor and prior knowledge of experts to be implemented. We present a learning-based co-speech gesture generation that is learned from 52 h of TED talks. The proposed end-to-end neural network model consists of an encoder for speech text understanding and a decoder to generate a sequence of gestures. The model successfully produces various gestures including iconic, metaphoric, deictic, and beat gestures. In a subjective evaluation, participants reported that the gestures were human-like and matched the speech content. We also demonstrate a co-speech gesture with a NAO robot working in real time., Comment: 7 pages; video and dataset: https://sites.google.com/view/youngwoo-yoon/projects/co-speech-gesture-generation

This work presents deep asymmetric networks with a set of node-wise variant activation functions. The nodes' sensitivities are affected by activation function selections such that the nodes with smaller indices become increasingly more sensitive. As a result, features learned by the nodes are sorted by the node indices in the order of their importance. Asymmetric networks not only learn input features but also the importance of those features. Nodes of lesser importance in asymmetric networks can be pruned to reduce the complexity of the networks, and the pruned networks can be retrained without incurring performance losses. We validate the feature-sorting property using both shallow and deep asymmetric networks as well as deep asymmetric networks transferred from famous networks.

We propose doubly nested network(DNNet) where all neurons represent their own sub-models that solve the same task. Every sub-model is nested both layer-wise and channel-wise. While nesting sub-models layer-wise is straight-forward with deep-supervision as proposed in \cite{xie2015holistically}, channel-wise nesting has not been explored in the literature to our best knowledge. Channel-wise nesting is non-trivial as neurons between consecutive layers are all connected to each other. In this work, we introduce a technique to solve this problem by sorting channels topologically and connecting neurons accordingly. For the purpose, channel-causal convolutions are used. Slicing doubly nested network gives a working sub-network. The most notable application of our proposed network structure with slicing operation is resource-efficient inference. At test time, computing resources such as time and memory available for running the prediction algorithm can significantly vary across devices and applications. Given a budget constraint, we can slice the network accordingly and use a sub-model for inference within budget, requiring no additional computation such as training or fine-tuning after deployment. We demonstrate the effectiveness of our approach in several practical scenarios of utilizing available resource efficiently.

Fully automating machine learning pipelines is one of the key challenges of current artificial intelligence research, since practical machine learning often requires costly and time-consuming human-powered processes such as model design, algorithm development, and hyperparameter tuning. In this paper, we verify that automated architecture search synergizes with the effect of gradient-based meta learning. We adopt the progressive neural architecture search \cite{liu:pnas_google:DBLP:journals/corr/abs-1712-00559} to find optimal architectures for meta-learners. The gradient based meta-learner whose architecture was automatically found achieved state-of-the-art results on the 5-shot 5-way Mini-ImageNet classification problem with $74.65\%$ accuracy, which is $11.54\%$ improvement over the result obtained by the first gradient-based meta-learner called MAML \cite{finn:maml:DBLP:conf/icml/FinnAL17}. To our best knowledge, this work is the first successful neural architecture search implementation in the context of meta learning., Comment: Presented at NIPS 2018 Workshop on Meta-Learning (MetaLearn 2018)

Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting. Although simply replaying all previous data alleviates the problem, it requires large memory and even worse, often infeasible in real world applications where the access to past data is limited. Inspired by the generative nature of hippocampus as a short-term memory system in primate brain, we propose the Deep Generative Replay, a novel framework with a cooperative dual model architecture consisting of a deep generative model ("generator") and a task solving model ("solver"). With only these two models, training data for previous tasks can easily be sampled and interleaved with those for a new task. We test our methods in several sequential learning settings involving image classification tasks., Comment: NIPS 2017