Search Results for author: Ryo Yonetani

Found 16 papers, 7 papers with code

CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces

1 code implementation24 Jan 2022 Keisuke Okumura, Ryo Yonetani, Mai Nishimura, Asako Kanezaki

Multi-agent path planning (MAPP) in continuous spaces is a challenging problem with significant practical importance.

ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives

2 code implementations8 Dec 2021 Toshinori Kitamura, Ryo Yonetani

We present ShinRL, an open-source library specialized for the evaluation of reinforcement learning (RL) algorithms from both theoretical and practical perspectives.

Q-Learning

Path Planning using Neural A* Search

2 code implementations16 Sep 2020 Ryo Yonetani, Tatsunori Taniai, Mohammadamin Barekatain, Mai Nishimura, Asako Kanezaki

We present Neural A*, a novel data-driven search method for path planning problems.

Adaptive Distillation for Decentralized Learning from Heterogeneous Clients

no code implementations18 Aug 2020 Jiaxin Ma, Ryo Yonetani, Zahid Iqbal

This paper addresses the problem of decentralized learning to achieve a high-performance global model by asking a group of clients to share local models pre-trained with their own data resources.

Federated Learning

L2B: Learning to Balance the Safety-Efficiency Trade-off in Interactive Crowd-aware Robot Navigation

1 code implementation20 Mar 2020 Mai Nishimura, Ryo Yonetani

This work presents a deep reinforcement learning framework for interactive navigation in a crowded place.

Robot Navigation

Crowd Density Forecasting by Modeling Patch-based Dynamics

no code implementations22 Nov 2019 Hiroaki Minoura, Ryo Yonetani, Mai Nishimura, Yoshitaka Ushiku

To address this task, we have developed the patch-based density forecasting network (PDFN), which enables forecasting over a sequence of crowd density maps describing how crowded each location is in each video frame.

Autonomous Driving Frame

MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics

2 code implementations28 Sep 2019 Mohammadamin Barekatain, Ryo Yonetani, Masashi Hamaya

Transfer reinforcement learning (RL) aims at improving the learning efficiency of an agent by exploiting knowledge from other source agents trained on relevant tasks.

reinforcement-learning Transfer Reinforcement Learning

Decentralized Learning of Generative Adversarial Networks from Non-iid Data

no code implementations23 May 2019 Ryo Yonetani, Tomohiro Takahashi, Atsushi Hashimoto, Yoshitaka Ushiku

This work addresses a new problem that learns generative adversarial networks (GANs) from multiple data collections that are each i) owned separately by different clients and ii) drawn from a non-identical distribution that comprises different classes.

Image Generation

Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data

no code implementations17 May 2019 Naoya Yoshida, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto, Ryo Yonetani

Therefore, to mitigate the degradation induced by non-IID data, we assume that a limited number (e. g., less than 1%) of clients allow their data to be uploaded to a server, and we propose a hybrid learning mechanism referred to as Hybrid-FL, wherein the server updates the model using the data gathered from the clients and aggregates the model with the models trained by clients.

Federated Learning

MGpi: A Computational Model of Multiagent Group Perception and Interaction

1 code implementation4 Mar 2019 Navyata Sanghvi, Ryo Yonetani, Kris Kitani

Toward enabling next-generation robots capable of socially intelligent interaction with humans, we present a $\mathbf{computational\; model}$ of interactions in a social environment of multiple agents and multiple groups.

Imitation Learning

Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

no code implementations23 Apr 2018 Takayuki Nishio, Ryo Yonetani

Specifically, FedCS solves a client selection problem with resource constraints, which allows the server to aggregate as many client updates as possible and to accelerate performance improvement in ML models.

Edge-computing Federated Learning

Future Person Localization in First-Person Videos

1 code implementation CVPR 2018 Takuma Yagi, Karttikeya Mangalam, Ryo Yonetani, Yoichi Sato

We present a new task that predicts future locations of people observed in first-person videos.

Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption

no code implementations ICCV 2017 Ryo Yonetani, Vishnu Naresh Boddeti, Kris M. Kitani, Yoichi Sato

We propose a privacy-preserving framework for learning visual classifiers by leveraging distributed private image data.

Ego-Surfing: Person Localization in First-Person Videos Using Ego-Motion Signatures

no code implementations15 Jun 2016 Ryo Yonetani, Kris M. Kitani, Yoichi Sato

We envision a future time when wearable cameras are worn by the masses and recording first-person point-of-view videos of everyday life.

Video Retrieval

Recognizing Micro-Actions and Reactions From Paired Egocentric Videos

no code implementations CVPR 2016 Ryo Yonetani, Kris M. Kitani, Yoichi Sato

We aim to understand the dynamics of social interactions between two people by recognizing their actions and reactions using a head-mounted camera.

Video Summarization

Ego-Surfing First-Person Videos

no code implementations CVPR 2015 Ryo Yonetani, Kris M. Kitani, Yoichi Sato

We incorporate this feature into our proposed approach that computes the motion correlation over supervoxel hierarchies to localize target instances in observer videos.

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