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Yusuke Mukuta

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18 papers
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18

ICML Conference 2025 Conference Paper

Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning

  • Motoki Omura
  • Kazuki Ota
  • Takayuki Osa
  • Yusuke Mukuta
  • Tatsuya Harada

For continuous action spaces, actor-critic methods are widely used in online reinforcement learning (RL). However, unlike RL algorithms for discrete actions, which generally model the optimal value function using the Bellman optimality operator, RL algorithms for continuous actions typically model Q-values for the current policy using the Bellman operator. These algorithms for continuous actions rely exclusively on policy updates for improvement, which often results in low sample efficiency. This study examines the effectiveness of incorporating the Bellman optimality operator into actor-critic frameworks. Experiments in a simple environment show that modeling optimal values accelerates learning but leads to overestimation bias. To address this, we propose an annealing approach that gradually transitions from the Bellman optimality operator to the Bellman operator, thereby accelerating learning while mitigating bias. Our method, combined with TD3 and SAC, significantly outperforms existing approaches across various locomotion and manipulation tasks, demonstrating improved performance and robustness to hyperparameters related to optimality. The code for this study is available at https: //github. com/motokiomura/annealed-q-learning.

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TMLR Journal 2025 Journal Article

HyperVQ: MLR-based Vector Quantization in Hyperbolic Space

  • Nabarun Goswami
  • Yusuke Mukuta
  • Tatsuya Harada

The success of models operating on tokenized data has heightened the need for effective tokenization methods, particularly in vision and auditory tasks where inputs are naturally continuous. A common solution is to employ Vector Quantization (VQ) within VQ Variational Autoencoders (VQVAEs), transforming inputs into discrete tokens by clustering embeddings in Euclidean space. However, Euclidean embeddings not only suffer from inefficient packing and limited separation—due to their polynomial volume growth—but are also prone to codebook collapse, where only a small subset of codebook vectors are effectively utilized. To address these limitations, we introduce HyperVQ, a novel approach that formulates VQ as a hyperbolic Multinomial Logistic Regression (MLR) problem, leveraging the exponential volume growth in hyperbolic space to mitigate collapse and improve cluster separability. Additionally, HyperVQ represents codebook vectors as geometric representatives of hyperbolic decision hyperplanes, encouraging disentangled and robust latent representations. Our experiments demonstrate that HyperVQ matches traditional VQ in generative and reconstruction tasks, while surpassing it in discriminative performance and yielding a more efficient and disentangled codebook.

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NeurIPS Conference 2025 Conference Paper

Intend to Move: A Multimodal Dataset for Intention-Aware Human Motion Understanding

  • Ryo Umagami
  • Liu Yue
  • Xuangeng Chu
  • Ryuto Fukushima
  • Tetsuya Narita
  • Yusuke Mukuta
  • Tomoyuki Takahata
  • Jianfei Yang

Human motion is inherently intentional, yet most motion modeling paradigms focus on low-level kinematics, overlooking the semantic and causal factors that drive behavior. Existing datasets further limit progress: they capture short, decontextualized actions in static scenes, providing little grounding for embodied reasoning. To address these limitations, we introduce $\textit{Intend to Move (I2M)}$, a large-scale, multimodal dataset for intention-grounded motion modeling. I2M contains 10. 1 hours of two-person 3D motion sequences recorded in dynamic realistic home environments, accompanied by multi-view RGB-D video, 3D scene geometry, and language annotations of each participant’s evolving intentions. Benchmark experiments reveal a fundamental gap in current motion models: they fail to translate high-level goals into physically and socially coherent motion. I2M thus serves not only as a dataset but as a benchmark for embodied intelligence, enabling research on models that can reason about, predict, and act upon the ``why'' behind human motion.

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RLJ Journal 2025 Journal Article

Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps

  • Motoki Omura
  • Yusuke Mukuta
  • Kazuki Ota
  • Takayuki Osa
  • Tatsuya Harada

Offline reinforcement learning (RL) aims to learn an optimal policy from a static dataset, making it particularly valuable in scenarios where data collection is costly, such as robotics. A major challenge in offline RL is distributional shift, where the learned policy deviates from the dataset distribution, potentially leading to unreliable out-of-distribution actions. To mitigate this issue, regularization techniques have been employed. While many existing methods utilize density ratio-based measures, such as the $f$-divergence, for regularization, we propose an approach that utilizes the Wasserstein distance, which is robust to out-of-distribution data and captures the similarity between actions. Our method employs input-convex neural networks (ICNNs) to model optimal transport maps, enabling the computation of the Wasserstein distance in a discriminator-free manner, thereby avoiding adversarial training and ensuring stable learning. Our approach demonstrates comparable or superior performance to widely used existing methods on the D4RL benchmark dataset. The code is available at [https://github.com/motokiomura/Q-DOT](url).

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RLC Conference 2025 Conference Paper

Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps

  • Motoki Omura
  • Yusuke Mukuta
  • Kazuki Ota
  • Takayuki Osa
  • Tatsuya Harada

Offline reinforcement learning (RL) aims to learn an optimal policy from a static dataset, making it particularly valuable in scenarios where data collection is costly, such as robotics. A major challenge in offline RL is distributional shift, where the learned policy deviates from the dataset distribution, potentially leading to unreliable out-of-distribution actions. To mitigate this issue, regularization techniques have been employed. While many existing methods utilize density ratio-based measures, such as the $f$-divergence, for regularization, we propose an approach that utilizes the Wasserstein distance, which is robust to out-of-distribution data and captures the similarity between actions. Our method employs input-convex neural networks (ICNNs) to model optimal transport maps, enabling the computation of the Wasserstein distance in a discriminator-free manner, thereby avoiding adversarial training and ensuring stable learning. Our approach demonstrates comparable or superior performance to widely used existing methods on the D4RL benchmark dataset. The code is available at [https: //github. com/motokiomura/Q-DOT](url).

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RLC Conference 2024 Conference Paper

Stabilizing Extreme Q-learning by Maclaurin Expansion

  • Motoki Omura
  • Takayuki Osa
  • Yusuke Mukuta
  • Tatsuya Harada

In offline reinforcement learning, in-sample learning methods have been widely used to prevent performance degradation caused by evaluating out-of-distribution actions from the dataset. Extreme Q-learning (XQL) employs a loss function based on the assumption that Bellman error follows a Gumbel distribution, enabling it to model the soft optimal value function in an in-sample manner. It has demonstrated strong performance in both offline and online reinforcement learning settings. However, issues remain, such as the instability caused by the exponential term in the loss function and the risk of the error distribution deviating from the Gumbel distribution. Therefore, we propose Maclaurin Expanded Extreme Q-learning to enhance stability. In this method, applying Maclaurin expansion to the loss function in XQL enhances stability against large errors. This approach involves adjusting the modeled value function between the value function under the behavior policy and the soft optimal value function, thus achieving a trade-off between stability and optimality depending on the order of expansion. It also enables adjustment of the error distribution assumption from a normal distribution to a Gumbel distribution. Our method significantly stabilizes learning in online RL tasks from DM Control, where XQL was previously unstable. Additionally, it improves performance in several offline RL tasks from D4RL.

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RLJ Journal 2024 Journal Article

Stabilizing Extreme Q-learning by Maclaurin Expansion

  • Motoki Omura
  • Takayuki Osa
  • Yusuke Mukuta
  • Tatsuya Harada

In offline reinforcement learning, in-sample learning methods have been widely used to prevent performance degradation caused by evaluating out-of-distribution actions from the dataset. Extreme Q-learning (XQL) employs a loss function based on the assumption that Bellman error follows a Gumbel distribution, enabling it to model the soft optimal value function in an in-sample manner. It has demonstrated strong performance in both offline and online reinforcement learning settings. However, issues remain, such as the instability caused by the exponential term in the loss function and the risk of the error distribution deviating from the Gumbel distribution. Therefore, we propose Maclaurin Expanded Extreme Q-learning to enhance stability. In this method, applying Maclaurin expansion to the loss function in XQL enhances stability against large errors. This approach involves adjusting the modeled value function between the value function under the behavior policy and the soft optimal value function, thus achieving a trade-off between stability and optimality depending on the order of expansion. It also enables adjustment of the error distribution assumption from a normal distribution to a Gumbel distribution. Our method significantly stabilizes learning in online RL tasks from DM Control, where XQL was previously unstable. Additionally, it improves performance in several offline RL tasks from D4RL.

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AAAI Conference 2024 Conference Paper

Symmetric Q-learning: Reducing Skewness of Bellman Error in Online Reinforcement Learning

  • Motoki Omura
  • Takayuki Osa
  • Yusuke Mukuta
  • Tatsuya Harada

In deep reinforcement learning, estimating the value function to evaluate the quality of states and actions is essential. The value function is often trained using the least squares method, which implicitly assumes a Gaussian error distribution. However, a recent study suggested that the error distribution for training the value function is often skewed because of the properties of the Bellman operator, and violates the implicit assumption of normal error distribution in the least squares method. To address this, we proposed a method called Symmetric Q-learning, in which the synthetic noise generated from a zero-mean distribution is added to the target values to generate a Gaussian error distribution. We evaluated the proposed method on continuous control benchmark tasks in MuJoCo. It improved the sample efficiency of a state-of-the-art reinforcement learning method by reducing the skewness of the error distribution.

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TMLR Journal 2023 Journal Article

Invariant Feature Coding using Tensor Product Representation

  • Yusuke Mukuta
  • Tatsuya Harada

In this study, a novel feature coding method that exploits invariance for transformations represented by a finite group of orthogonal matrices is proposed. We prove that the group-invariant feature vector contains sufficient discriminative information when learning a linear classifier using convex loss minimization. Based on this result, a novel feature model that explicitly considers group action is proposed for principal component analysis and k-means clustering, which are commonly used in most feature coding methods, and global feature functions. Although the global feature functions are in general complex nonlinear functions, the group action on this space can be easily calculated by constructing these functions as tensor-product representations of basic representations, resulting in an explicit form of invariant feature functions. The effectiveness of our method is demonstrated on several image datasets.

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AAAI Conference 2022 Conference Paper

Fully Spiking Variational Autoencoder

  • Hiromichi Kamata
  • Yusuke Mukuta
  • Tatsuya Harada

Spiking neural networks (SNNs) can be run on neuromorphic devices with ultra-high speed and ultra-low energy consumption because of their binary and event-driven nature. Therefore, SNNs are expected to have various applications, including as generative models being running on edge devices to create high-quality images. In this study, we build a variational autoencoder (VAE) with SNN to enable image generation. VAE is known for its stability among generative models; recently, its quality advanced. In vanilla VAE, the latent space is represented as a normal distribution, and floating-point calculations are required in sampling. However, this is not possible in SNNs because all features must be binary time series data. Therefore, we constructed the latent space with an autoregressive SNN model, and randomly selected samples from its output to sample the latent variables. This allows the latent variables to follow the Bernoulli process and allows variational learning. Thus, we build the Fully Spiking Variational Autoencoder where all modules are constructed with SNN. To the best of our knowledge, we are the first to build a VAE only with SNN layers. We experimented with several datasets, and confirmed that it can generate images with the same or better quality compared to conventional ANNs. The code is available at https: //github. com/kamata1729/FullySpikingVAE.

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ICLR Conference 2021 Conference Paper

Hyperbolic Neural Networks++

  • Ryohei Shimizu
  • Yusuke Mukuta
  • Tatsuya Harada

Hyperbolic spaces, which have the capacity to embed tree structures without distortion owing to their exponential volume growth, have recently been applied to machine learning to better capture the hierarchical nature of data. In this study, we generalize the fundamental components of neural networks in a single hyperbolic geometry model, namely, the Poincaré ball model. This novel methodology constructs a multinomial logistic regression, fully-connected layers, convolutional layers, and attention mechanisms under a unified mathematical interpretation, without increasing the parameters. Experiments show the superior parameter efficiency of our methods compared to conventional hyperbolic components, and stability and outperformance over their Euclidean counterparts.

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ICRA Conference 2021 Conference Paper

Real-Time Mesh Extraction from Implicit Functions via Direct Reconstruction of Decision Boundary

  • Wataru Kawai
  • Yusuke Mukuta
  • Tatsuya Harada

The ability to estimate 3D object shape from a single image is vital to robotics and manufacturing. For instance, it enables iterative trial-and-error in simulated environments. In single-view reconstruction, implicit functions have demonstrated superior results over traditional methods. However, implicit functions suffer from the heavy computation of mesh extraction. This is due to the indirect mesh extraction, where the number of evaluation points grows cubically with resolution. On the other hand, reducing the resolution results in the discretization error of marching cubes (MC). In this work, we aim to perform efficient and accurate mesh extraction from implicit functions. The idea is to directly reconstruct the decision boundary of implicit functions as a mesh by reverse tracing from the output. It eliminates the need for evaluating massive points and error-prone MC. Consequently, we propose implementing an implicit function via a composite function of a flow and Binary-coded Input Neural Network (BCINN). The boundary of BCINN is easily identifiable, and the flow is invertible. Owing to these properties, the decision boundary of the composite function can be directly and efficiently reconstructed. In our experiments, we demonstrate that the proposed method significantly improves runtime/memory efficiency, with results comparable to those of existing methods. Specifically, our method enables real-time high-quality mesh inference from a single image.

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AAAI Conference 2021 Conference Paper

Spherical Image Generation from a Single Image by Considering Scene Symmetry

  • Takayuki Hara
  • Yusuke Mukuta
  • Tatsuya Harada

Spherical images taken in all directions (360◦ ×180◦ ) allow the full surroundings of a subject to be represented, providing an immersive experience to viewers. Generating a spherical image from a single normal-field-of-view (NFOV) image is convenient and expands the usage scenarios considerably without relying on a specific panoramic camera or images taken from multiple directions; however, achieving such images remains a challenging and unresolved problem. The primary challenge is controlling the high degree of freedom involved in generating a wide area that includes all directions of the desired spherical image. We focus on scene symmetry, which is a basic property of the global structure of spherical images, such as rotational symmetry, plane symmetry, and asymmetry. We propose a method for generating a spherical image from a single NFOV image and controlling the degree of freedom of the generated regions using the scene symmetry. To estimate and control the scene symmetry using both a circular shift and flip of the latent image features, we incorporate the intensity of the symmetry as a latent variable into conditional variational autoencoders. Our experiments show that the proposed method can generate various plausible spherical images controlled from symmetric to asymmetric, and can reduce the reconstruction errors of the generated images based on the estimated symmetry.

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NeurIPS Conference 2020 Conference Paper

Neural Star Domain as Primitive Representation

  • Yuki Kawana
  • Yusuke Mukuta
  • Tatsuya Harada

Reconstructing 3D objects from 2D images is a fundamental task in computer vision. Acurate structured reconstruction by parsimonious and semantic primitive representation further broadens its application. When reconstructing a target shape with multiple primitives, it is preferable that one can instantly access the union of basic properties of the shape such as collective volume and surface, treating the primitives as if they are one single shape. This becomes possible by primitive representation with unified implicit and explicit representations. However, primitive representations in current approaches do not satisfy all of the above requirements at the same time. To solve this problem, we propose a novel primitive representation named neural star domain (NSD) that learns primitive shapes in the star domain. We show that NSD is a universal approximator of the star domain and is not only parsimonious and semantic but also an implicit and explicit shape representation. We demonstrate that our approach outperforms existing methods in image reconstruction tasks, semantic capabilities, and speed and quality of sampling high-resolution meshes.

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AAAI Conference 2019 Conference Paper

Estimating the Causal Effect from Partially Observed Time Series

  • Akane Iseki
  • Yusuke Mukuta
  • Yoshitaka Ushiku
  • Tatsuya Harada

Many real-world systems involve interacting time series. The ability to detect causal dependencies between system components from observed time series of their outputs is essential for understanding system behavior. The quantification of causal influences between time series is based on the definition of some causality measure. Partial Canonical Correlation Analysis (Partial CCA) and its extensions are examples of methods used for robustly estimating the causal relationships between two multidimensional time series even when the time series are short. These methods assume that the input data are complete and have no missing values. However, real-world data often contain missing values. It is therefore crucial to estimate the causality measure robustly even when the input time series is incomplete. Treating this problem as a semi-supervised learning problem, we propose a novel semi-supervised extension of probabilistic Partial CCA called semi-Bayesian Partial CCA. Our method exploits the information in samples with missing values to prevent the overfitting of parameter estimation even when there are few complete samples. Experiments based on synthesized and real data demonstrate the ability of the proposed method to estimate causal relationships more correctly than existing methods when the data contain missing values, the dimensionality is large, and the number of samples is small.

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AAAI Conference 2018 Conference Paper

Alternating Circulant Random Features for Semigroup Kernels

  • Yusuke Mukuta
  • Yoshitaka Ushiku
  • Tatsuya Harada

The random features method is an efficient method to approximate the kernel function. In this paper, we propose novel random features called “alternating circulant random features, ” which consist of a random mixture of independent random structured matrices. Existing fast random features exploit random sign flipping to reduce the correlation between features. Sign flipping works well on random Fourier features for real-valued shift-invariant kernels because the corresponding weight distribution is symmetric. However, this method cannot be applied to random Laplace features directly because the distribution is not symmetric. The method proposed herein yields alternating circulant random features, with the correlation between features being reduced through the random sampling of weights from multiple independent random structured matrices instead of via random sign flipping. The proposed method facilitates rapid calculation by employing structured matrices. In addition, the weight distribution is preserved because sign flipping is not implemented. The performance of the proposed alternating circulant random features method is theoretically and empirically evaluated.

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AAAI Conference 2018 Conference Paper

Weakly Supervised Collective Feature Learning From Curated Media

  • Yusuke Mukuta
  • Akisato Kimura
  • David Adrian
  • Zoubin Ghahramani

The current state-of-the-art in feature learning relies on the supervised learning of large-scale datasets consisting of target content items and their respective category labels. However, constructing such large-scale fully-labeled datasets generally requires painstaking manual effort. One possible solution to this problem is to employ community contributed text tags as weak labels, however, the concepts underlying a single text tag strongly depends on the users. We instead present a new paradigm for learning discriminative features by making full use of the human curation process on social networking services (SNSs). During the process of content curation, SNS users collect content items manually from various sources and group them by context, all for their own benefit. Due to the nature of this process, we can assume that (1) content items in the same group share the same semantic concept and (2) groups sharing the same images might have related semantic concepts. Through these insights, we can define human curated groups as weak labels from which our proposed framework can learn discriminative features as a representation in the space of semantic concepts the users intended when creating the groups. We show that this feature learning can be formulated as a problem of link prediction for a bipartite graph whose nodes corresponds to content items and human curated groups, and propose a novel method for feature learning based on sparse coding or network fine-tuning.

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ICML Conference 2014 Conference Paper

Probabilistic Partial Canonical Correlation Analysis

  • Yusuke Mukuta
  • Tatsuya Harada

Partial canonical correlation analysis (partial CCA) is a statistical method that estimates a pair of linear projections onto a low dimensional space, where the correlation between two multidimensional variables is maximized after eliminating the influence of a third variable. Partial CCA is known to be closely related to a causality measure between two time series. However, partial CCA requires the inverses of covariance matrices, so the calculation is not stable. This is particularly the case for high-dimensional data or small sample sizes. Additionally, we cannot estimate the optimal dimension of the subspace in the model. In this paper, we have addressed these problems by proposing a probabilistic interpretation of partial CCA and deriving a Bayesian estimation method based on the probabilistic model. Our numerical experiments demonstrated that our methods can stably estimate the model parameters, even in high dimensions or when there are a small number of samples.

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