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Yusuke Tanaka

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18 papers

2 author rows

ICRA Conference 2025 Conference Paper

Cycloidal Quasi-Direct Drive Actuator Designs with Learning-Based Torque Estimation for Legged Robotics

Alvin Zhu
Yusuke Tanaka
Fadi Rafeedi
Dennis W. Hong

This paper presents a novel approach through the design and implementation of Cycloidal Quasi-Direct Drive actuators for legged robotics. The cycloidal gear mechanism, with its inherent high torque density and mechanical robustness, offers significant advantages over conventional designs. By integrating cycloidal gears into the Quasi-Direct Drive framework, we aim to enhance the performance of legged robots, particularly in tasks demanding high torque and dynamic loads, while still keeping them lightweight. Additionally, we develop a torque estimation framework for the actuator using an Actuator Network, which effectively reduces the sim-toreal gap introduced by the cycloidal drive's complex dynamics. This integration is crucial for capturing the complex dynamics of a cycloidal drive, which contributes to improved learning efficiency, agility, and adaptability for reinforcement learning.

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ICRA Conference 2025 Conference Paper

Mechanisms and Computational Design of Multi-Modal End-Effector with Force Sensing Using Gated Networks

Yusuke Tanaka
Alvin Zhu
Richard Lin
Ankur Mehta
Dennis W. Hong

In limbed robotics, end-effectors must serve dual functions, such as both feet for locomotion and grippers for grasping, which presents design challenges. This paper introduces a multi-modal end-effector capable of transitioning between flat and line foot configurations while providing grasping capabilities. MAGPIE integrates eight-axis force sensing using proposed mechanisms with Hall effect sensors, enabling both contact and tactile force measurements. We present a computational design framework for our sensing mechanism that accounts for noise and interference, allowing for desired sensitivity and force ranges and generating ideal inverse models. The hardware implementation of MAGPIE is validated through experiments, demonstrating its capability as a foot and verifying the performance of the sensing mechanisms, ideal models, and gated network-based models.

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TMLR Journal 2025 Journal Article

Meta-Learning for Graphs with Heterogeneous Node Attribute Spaces for Few-Shot Edge Predictions

Zhong Chuang
Yusuke Tanaka
Tomoharu Iwata

Prediction of edges between nodes in graph data is useful for many applications, such as social network analysis and knowledge graph completion. Existing graph neural network-based approaches have achieved notable advancements, but encounter significant difficulty in building an effective model when there is an insufficient number of known edges in graphs. Although some meta-learning approaches were introduced to solve this problem, having an assumption that the nodes of training graphs and test graphs are in homogeneous attribute spaces, which limits the flexibility of applications. In this paper, we proposed a meta-learning method for edge prediction that can learn from graphs with nodes in heterogeneous attribute spaces. The proposed model consists of attribute-wise message-passing networks that transform information between connected nodes for each attribute, resulting in attribute-specific node embeddings. The node embeddings are obtained by calculating the mean of the attribute-specific node embeddings.The encoding operation can be repeated multiple times to capture complex patterns. The attribute-wise message-passing networks are shared across all graphs, allowing knowledge transfer between different graphs.The probabilities of edges are estimated by the Euclidian distance between node embeddings. Experimental results on 14 real-world data sets demonstrate that the proposed method outperforms existing methods in edge prediction problems with sparse edge information.

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TMLR Journal 2024 Journal Article

Meta-Learning under Task Shift

Lei Sun
Yusuke Tanaka
Tomoharu Iwata

A common assumption in meta-learning is that meta-training and meta-test tasks are drawn from the same distribution. However, this assumption is often not fulfilled. Under such task shift, standard meta-learning algorithms do not work as desired since their unbiasedness is no longer maintained. In this paper, we propose a new meta-learning method called Importance Weighted Meta-Learning (IWML), which preserves unbiasedness even under task shift. Our approach uses both labeled meta-training datasets and unlabeled datasets in tasks obtained from the meta-test task distribution to assign weights to each meta-training task. These weights are determined by the ratio of meta-test and meta-training task densities. Our method enables the model to focus more on the meta-training tasks that closely align with meta-test tasks during the meta-training process. We meta-learn neural network-based models by minimizing the expected weighted meta-training error, which is an unbiased estimator of the expected error over meta-test tasks. The task density ratio is estimated using kernel density estimation, where the distance between tasks is measured by the maximum mean discrepancy. Our empirical evaluation of few-shot classification datasets demonstrates a significant improvement of IWML over existing approaches.

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ICRA Conference 2024 Conference Paper

OptiState: State Estimation of Legged Robots using Gated Networks with Transformer-based Vision and Kalman Filtering

Alexander Schperberg
Yusuke Tanaka
Saviz Mowlavi
Feng Xu
Bharathan Balaji
Dennis W. Hong

State estimation for legged robots is challenging due to their highly dynamic motion and limitations imposed by sensor accuracy. By integrating Kalman filtering, optimization, and learning-based modalities, we propose a hybrid solution that combines proprioception and exteroceptive information for estimating the state of the robot’s trunk. Leveraging joint encoder and IMU measurements, our Kalman filter is enhanced through a single-rigid body model that incorporates ground reaction force control outputs from convex Model Predictive Control optimization. The estimation is further refined through Gated Recurrent Units, which also considers semantic insights and robot height from a Vision Transformer autoencoder applied on depth images. This framework not only furnishes accurate robot state estimates, including uncertainty evaluations, but can minimize the nonlinear errors that arise from sensor measurements and model simplifications through learning. The proposed methodology is evaluated in hardware using a quadruped robot on various terrains, yielding a 65% improvement on the Root Mean Squared Error compared to our VIO SLAM baseline. Code example: https://github.com/AlexS28/OptiState

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IJCAI Conference 2024 Conference Paper

Symplectic Neural Gaussian Processes for Meta-learning Hamiltonian Dynamics

Tomoharu Iwata
Yusuke Tanaka

We propose a meta-learning method for modeling Hamiltonian dynamics from a limited number of data. Although Hamiltonian neural networks have been successfully used for modeling dynamics that obey the energy conservation law, they require many data to achieve high performance. The proposed method meta-learns our neural network-based model using datasets in various dynamical systems, such that our model can predict vector fields of unseen systems. In our model, a system representation is inferred from given small data using an encoder network. Then, the system-specific vector field is predicted by modeling the Hamiltonian using a Gaussian process (GP) with neural network-based mean and kernel functions that depend on the inferred system representation. This GP-based Hamiltonian allows us to analytically obtain predictions that are adapted to small data while imposing the constraint of the conservation law. The neural networks are shared across systems, which enables us to learn knowledge from multiple systems, and use it for unseen systems. In our experiments, we demonstrate that the proposed method outperforms existing methods for predicting dynamics from a small number of observations in target systems.

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NeurIPS Conference 2024 Conference Paper

Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective

Takeshi Koshizuka
Masahiro Fujisawa
Yusuke Tanaka
Issei Sato

In this paper, we explores the expressivity and trainability of the Fourier Neural Operator (FNO). We establish a mean-field theory for the FNO, analyzing the behavior of the random FNO from an \emph{edge of chaos} perspective. Our investigation into the expressivity of a random FNO involves examining the ordered-chaos phase transition of the network based on the weight distribution. This phase transition demonstrates characteristics unique to the FNO, induced by mode truncation, while also showcasing similarities to those of densely connected networks. Furthermore, we identify a connection between expressivity and trainability: the ordered and chaotic phases correspond to regions of vanishing and exploding gradients, respectively. This finding provides a practical prerequisite for the stable training of the FNO. Our experimental results corroborate our theoretical findings.

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ICRA Conference 2022 Conference Paper

Development of a Stereo-vision based High-throughput Robotic System for Mouse Tail Vein Injection

Tianyi Ko
Koichi Nishiwaki
Koji Terada
Yusuke Tanaka
Shun Mitsumata
Ryuichi Katagiri
Taketo Junko
Naoshi Horiba

In this paper, we present a robotic device for mouse tail vein injection. We propose a mouse holding mechanism to realize vein injection without anesthetizing the mouse, which consists of a tourniquet, vacuum port, and adaptive tail-end fixture. The position of the target vein in 3D space is reconstructed from a high-resolution stereo vision. The vein is detected by a simple but robust vein line detector. Thanks to the proposed two-staged calibration process, the total time for the injection process is limited to 1. 5 minutes, despite that the position of needle and tail vein varies for each trial. We performed an injection experiment targeting 40 mice and succeeded to inject saline to 37 of them, resulting 92. 5% success ratio.

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IROS Conference 2022 Conference Paper

SCALER: A Tough Versatile Quadruped Free-Climber Robot

Yusuke Tanaka
Yuki Shirai
Xuan Lin
Alexander Schperberg
Hayato Kato
Alexander Swerdlow
Naoya Kumagai
Dennis W. Hong

This paper introduces SCALER, a quadrupedal robot that demonstrates climbing on bouldering walls, over-hangs, ceilings and trotting on the ground. SCALER is one of the first high-degrees of freedom four-limbed robots that can free-climb under the Earth's gravity and one of the most mechanically efficient quadrupeds on the ground. Where other state-of-the-art climbers specialize in climbing, SCALER promises practical free-climbing with payload and ground locomotion, which realizes true versatile mobility. A new climbing gait, SKATE gait, increases the payload by utilizing the SCALER body linkage mechanism. SCALER achieves a maximum normalized locomotion speed of 1. 87 /s, or 0. 56 m/s on the ground and 1. 0 /min, or 0. 35 m/min in bouldering wall climbing. Payload capacity reaches 233 % of the SCALER weight on the ground and 35 % on the vertical wall. Our GOAT gripper, a mechanically adaptable underactuated two-finger gripper, successfully grasps convex and non-convex objects and supports SCALER.

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IROS Conference 2022 Conference Paper

Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots

Yuki Shirai
Xuan Lin
Alexander Schperberg
Yusuke Tanaka
Hayato Kato
Varit Vichathorn
Dennis W. Hong

While motion planning of locomotion for legged robots has shown great success, motion planning for legged robots with dexterous multi-finger grasping is not mature yet. We present an efficient motion planning framework for simultaneously solving locomotion (e. g. , centroidal dynamics), grasping (e. g. , patch contact), and contact (e. g. , gait) problems. To accelerate the planning process, we propose distributed optimization frameworks based on Alternating Direction Methods of Multipliers (ADMM) to solve the original large-scale Mixed-Integer NonLinear Programming (MINLP). The resulting frameworks use Mixed-Integer Quadratic Programming (MIQP) to solve contact and NonLinear Programming (NLP) to solve nonlinear dynamics, which are more computationally tractable and less sensitive to parameters. Also, we explicitly enforce patch contact constraints from limit surfaces with micro-spine grippers. We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45° with a much shorter planning time.

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NeurIPS Conference 2022 Conference Paper

Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data

Yusuke Tanaka
Tomoharu Iwata
Naonori Ueda

Hamiltonian mechanics is a well-established theory for modeling the time evolution of systems with conserved quantities (called Hamiltonian), such as the total energy of the system. Recent works have parameterized the Hamiltonian by machine learning models (e. g. , neural networks), allowing Hamiltonian dynamics to be obtained from state trajectories without explicit mathematical modeling. However, the performance of existing models is limited as we can observe only noisy and sparse trajectories in practice. This paper proposes a probabilistic model that can learn the dynamics of conservative or dissipative systems from noisy and sparse data. We introduce a Gaussian process that incorporates the symplectic geometric structure of Hamiltonian systems, which is used as a prior distribution for estimating Hamiltonian systems with additive dissipation. We then present its spectral representation, Symplectic Spectrum Gaussian Processes (SSGPs), for which we newly derive random Fourier features with symplectic structures. This allows us to construct an efficient variational inference algorithm for training the models while simulating the dynamics via ordinary differential equation solvers. Experiments on several physical systems show that SSGP offers excellent performance in predicting dynamics that follow the energy conservation or dissipation law from noisy and sparse data.

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IROS Conference 2021 Conference Paper

An Under-Actuated Whippletree Mechanism Gripper based on Multi-Objective Design Optimization with Auto-Tuned Weights

Yusuke Tanaka
Yuki Shirai
Zachary Lacey
Xuan Lin
Jane Liu
Dennis W. Hong

Current rigid linkage grippers are limited in flexibility, and gripper design optimality relies on expertise, experiments, or arbitrary parameters. Our proposed rigid gripper can accommodate irregular and off-center objects through a whippletree mechanism, improving adaptability. We present a whippletree-based rigid under-actuated gripper and its parametric design multi-objective optimization for a one-wall climbing task. Our proposed objective function considers kinematics and grasping forces simultaneously with a mathematical metric based on a model of an object environment. Our multi-objective problem is formulated as a single kinematic objective function with auto-tuning force-based weight. Our results indicate that our proposed objective function determines optimal parameters and kinematic ranges for our under-actuated gripper in the task environment with sufficient grasping forces.

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AIJ Journal 2021 Journal Article

Time-delayed collective flow diffusion models for inferring latent people flow from aggregated data at limited locations

Yusuke Tanaka
Tomoharu Iwata
Takeshi Kurashima
Hiroyuki Toda
Naonori Ueda
Toshiyuki Tanaka

The rapid adoption of wireless sensor devices has made it easier to record location information of people in a variety of spaces (e. g. , exhibition halls). Location information is often aggregated due to privacy and/or cost concerns. The aggregated data we use as input consist of the numbers of incoming and outgoing people at each location and at each time step. Since the aggregated data lack tracking information of individuals, determining the flow of people between locations is not straightforward. In this article, we address the problem of inferring latent people flows, that is, transition populations between locations, from just aggregated population data gathered from observed locations. Existing models assume that everyone is always in one of the observed locations at every time step; this, however, is an unrealistic assumption, because we do not always have a large enough number of sensor devices to cover the large-scale spaces targeted. To overcome this drawback, we propose a probabilistic model with flow conservation constraints that incorporate travel duration distributions between observed locations. To handle noisy settings, we adopt noisy observation models for the numbers of incoming and outgoing people, where the noise is regarded as a factor that may disturb flow conservation, e. g. , people may appear in or disappear from the predefined space of interest. We develop an approximate expectation-maximization (EM) algorithm that simultaneously estimates transition populations and model parameters. Our experiments demonstrate the effectiveness of the proposed model on real-world datasets of pedestrian data in exhibition halls, bike trip data and taxi trip data in New York City.

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AAAI Conference 2020 Conference Paper

Exact and Efficient Inference for Collective Flow Diffusion Model via Minimum Convex Cost Flow Algorithm

Yasunori Akagi
Takuya Nishimura
Yusuke Tanaka
Takeshi Kurashima
Hiroyuki Toda

Collective Flow Diffusion Model (CFDM) is a general framework to find the hidden movements underlying aggregated population data. The key procedure in CFDM analysis is MAP inference of hidden variables. Unfortunately, existing approaches fail to offer exact MAP inferences, only approximate versions, and take a lot of computation time when applied to large scale problems. In this paper, we propose an exact and efficient method for MAP inference in CFDM. Our key idea is formulating the MAP inference problem as a combinatorial optimization problem called Minimum Convex Cost Flow Problem (C-MCFP) with no approximation or continuous relaxation. On the basis of this formulation, we propose an efficient inference method that employs the C-MCFP algorithm as a subroutine. Our experiments on synthetic and real datasets show that the proposed method is effective both in single MAP inference and people flow estimation with EM algorithm.

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AAAI Conference 2019 Conference Paper

Refining Coarse-Grained Spatial Data Using Auxiliary Spatial Data Sets with Various Granularities

Yusuke Tanaka
Tomoharu Iwata
Toshiyuki Tanaka
Takeshi Kurashima
Maya Okawa
Hiroyuki Toda

We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data sets with various granularities by hierarchically incorporating Gaussian processes. With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity. The finegrained target data are modeled by another Gaussian process that considers both the spatial correlation and the auxiliary data sets with their uncertainty. We integrate the Gaussian process with a spatial aggregation process that transforms the fine-grained target data into the coarse-grained target data, by which we can infer the fine-grained target Gaussian process from the coarse-grained data. Our model is designed such that the inference of model parameters based on the exact marginal likelihood is possible, in which the variables of finegrained target and auxiliary data are analytically integrated out. Our experiments on real-world spatial data sets demonstrate the effectiveness of the proposed model.

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NeurIPS Conference 2019 Conference Paper

Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs

Yusuke Tanaka
Toshiyuki Tanaka
Tomoharu Iwata
Takeshi Kurashima
Maya Okawa
Yasunori Akagi
Hiroyuki Toda

We propose a probabilistic model for inferring the multivariate function from multiple areal data sets with various granularities. Here, the areal data are observed not at location points but at regions. Existing regression-based models can only utilize the sufficiently fine-grained auxiliary data sets on the same domain (e. g. , a city). With the proposed model, the functions for respective areal data sets are assumed to be a multivariate dependent Gaussian process (GP) that is modeled as a linear mixing of independent latent GPs. Sharing of latent GPs across multiple areal data sets allows us to effectively estimate the spatial correlation for each areal data set; moreover it can easily be extended to transfer learning across multiple domains. To handle the multivariate areal data, we design an observation model with a spatial aggregation process for each areal data set, which is an integral of the mixed GP over the corresponding region. By deriving the posterior GP, we can predict the data value at any location point by considering the spatial correlations and the dependences between areal data sets, simultaneously. Our experiments on real-world data sets demonstrate that our model can 1) accurately refine coarse-grained areal data, and 2) offer performance improvements by using the areal data sets from multiple domains.

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IJCAI Conference 2018 Conference Paper

Estimating Latent People Flow without Tracking Individuals

Yusuke Tanaka
Tomoharu Iwata
Takeshi Kurashima
Hiroyuki Toda
Naonori Ueda

Analyzing people flows is important for better navigation and location-based advertising. Since the location information of people is often aggregated for protecting privacy, it is not straightforward to estimate transition populations between locations from aggregated data. Here, aggregated data are incoming and outgoing people counts at each location; they do not contain tracking information of individuals. This paper proposes a probabilistic model for estimating unobserved transition populations between locations from only aggregated data. With the proposed model, temporal dynamics of people flows are assumed to be probabilistic diffusion processes over a network, where nodes are locations and edges are paths between locations. By maximizing the likelihood with flow conservation constraints that incorporate travel duration distributions between locations, our model can robustly estimate transition populations between locations. The statistically significant improvement of our model is demonstrated using real-world datasets of pedestrian data in exhibition halls, bike trip data and taxi trip data in New York City.

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YNIMG Journal 2010 Journal Article

Investigating association of brain volumes with intracranial capacity in schizophrenia

Keita Ueda
Hironobu Fujiwara
Jun Miyata
Kazuyuki Hirao
Teruyasu Saze
Ryosaku Kawada
Shinsuke Fujimoto
Yusuke Tanaka

Intracranial volume (ICV) is usually treated as a global or nuisance covariate in almost all volumetric studies of schizophrenia. However, validation for this analytic method has seldom been accomplished. In this study, we aimed to determine the effects of ICV on gray matter (GM) and white matter (WM) volumes. Sixty-three patients with schizophrenia and sixty normal controls were recruited; and high resolution T1 weighted images were obtained by 3T-MRI. After segmentation and normalization of the images into GM, WM, and cerebrospinal fluid (CSF), multiple regression analyses of global GM and WM volumes were performed using explanatory variables such as diagnosis, ICV, and diagnosis-ICV interaction. In addition, associations between regional GM and WM volumes with ICV were also investigated using voxel-based morphometry (VBM). No significant interaction between diagnosis and ICV was found for global GM volume, whereas interactions were detected in restricted GM areas using VBM. On the other hand, an interaction between ICV and diagnosis was found in WM not only for regional volumes, but also for global WM volume. The regression slope of global WM volumes against ICV was steeper in patients with schizophrenia than in healthy controls. These results imply that ICV should be carefully evaluated in the analyses of volumetric studies of schizophrenia, especially when analyzing WM volumes.

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