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Satoshi Yagi

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4 papers

2 author rows

IROS Conference 2025 Conference Paper

VecNav: Vector Goal Robot Navigation from In-the-wild Videos

Ruixiang Cao
Satoshi Yagi
Satoshi Yamamori
Jun Morimoto

We propose VecNav, a novel approach that trains a monocular navigation model through self-supervision using uncalibrated, human-captured videos. These videos, characterized by unknown camera intrinsics and extrinsics, are readily available from video-sharing platforms (e. g. YouTube) and are referred to as "in-the-wild" videos due to their unregulated capture conditions. Our approach involves estimating ground truth trajectories from these videos using monocular visual odometry. We then train a transformer-based diffusion policy that takes a goal specified by a vector and RGB images as input and generates action predictions. Our method leverages a significantly larger and more diverse dataset compared to existing monocular visual navigation approaches. This diversity holds the potential to develop a generalist navigation model capable of guiding various types of robots in unfamiliar environments. We evaluated our method on a differential drive robot, demonstrating its capability to effectively navigate using solely "in-the-wild" videos for training. Our experiments demonstrate that VecNav successfully learned to act based on visual affordances, relying solely on uncalibrated "in-the-wild" data.

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ICRA Conference 2019 Conference Paper

Exploiting Human and Robot Muscle Synergies for Human-in-the-loop Optimization of EMG-based Assistive Strategies

Masashi Hamaya
Takamitsu Matsubara
Jun-ichiro Furukawa
Yuting Sun
Satoshi Yagi
Tatsuya Teramae
Tomoyuki Noda
Jun Morimoto

In this study, we propose a novel human-in-the-loop optimization approach for exoskeleton robot control. We develop a method to optimize widely-used Electromyography (EMG)-based assistive strategies. If we use multiple EMG channels to control multi-DoF robots, optimization process becomes complex and requires a large amount of data. To make the optimization tractable, we exploit the synergies both of the human muscles and artificial muscles of the exoskeleton robots to reduce the number of parameters of the assistive strategies. We show that we can extract the synergies not only from the user's muscle activities but from pneumatic artificial muscle (PAMs) contractions of the exoskeleton robot. Then, we adopt a Bayesian optimization method to acquire the parameters for assisting human movements by iteratively identifying the user's preferences of the assistive strategies. We conducted experiments to evaluate our proposed method with a PAMs-driven upper-limb exoskeleton robot. Our method successfully learned assistive strategies from the human-in-theloop optimization with a practicable number of interactions.

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AAAI Conference 2019 Conference Paper

Variational Autoencoder with Implicit Optimal Priors

Hiroshi Takahashi
Tomoharu Iwata
Yuki Yamanaka
Masanori Yamada
Satoshi Yagi

The variational autoencoder (VAE) is a powerful generative model that can estimate the probability of a data point by using latent variables. In the VAE, the posterior of the latent variable given the data point is regularized by the prior of the latent variable using Kullback Leibler (KL) divergence. Although the standard Gaussian distribution is usually used for the prior, this simple prior incurs over-regularization. As a sophisticated prior, the aggregated posterior has been introduced, which is the expectation of the posterior over the data distribution. This prior is optimal for the VAE in terms of maximizing the training objective function. However, KL divergence with the aggregated posterior cannot be calculated in a closed form, which prevents us from using this optimal prior. With the proposed method, we introduce the density ratio trick to estimate this KL divergence without modeling the aggregated posterior explicitly. Since the density ratio trick does not work well in high dimensions, we rewrite this KL divergence that contains the high-dimensional density ratio into the sum of the analytically calculable term and the lowdimensional density ratio term, to which the density ratio trick is applied. Experiments on various datasets show that the VAE with this implicit optimal prior achieves high density estimation performance.

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IJCAI Conference 2018 Conference Paper

Student-t Variational Autoencoder for Robust Density Estimation

Hiroshi Takahashi
Tomoharu Iwata
Yuki Yamanaka
Masanori Yamada
Satoshi Yagi

We propose a robust multivariate density estimator based on the variational autoencoder (VAE). The VAE is a powerful deep generative model, and used for multivariate density estimation. With the original VAE, the distribution of observed continuous variables is assumed to be a Gaussian, where its mean and variance are modeled by deep neural networks taking latent variables as their inputs. This distribution is called the decoder. However, the training of VAE often becomes unstable. One reason is that the decoder of VAE is sensitive to the error between the data point and its estimated mean when its estimated variance is almost zero. We solve this instability problem by making the decoder robust to the error using a Bayesian approach to the variance estimation: we set a prior for the variance of the Gaussian decoder, and marginalize it out analytically, which leads to proposing the Student-t VAE. Numerical experiments with various datasets show that training of the Student-t VAE is robust, and the Student-t VAE achieves high density estimation performance.

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