Ryo Yonetani | DeepAI

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Featured Co-authors

Kris Kitani
52 publications
Kris M. Kitani
40 publications
Vishnu Naresh Boddeti
37 publications
Takayuki Nishio
35 publications
Yoshitaka Ushiku
33 publications
Yoichi Sato
29 publications
Koji Yamamoto
29 publications
Masahiro Morikura
22 publications
Karttikeya Mangalam
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Tadashi Kozuno
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Keisuke Okumura
15 publications

∙ 05/19/2023

Counterfactual Fairness Filter for Fair-Delay Multi-Robot Navigation

Multi-robot navigation is the task of finding trajectories for a team of...

0 Hikaru Asano, et al. ∙

∙ 04/24/2023

When to Replan? An Adaptive Replanning Strategy for Autonomous Navigation using Deep Reinforcement Learning

The hierarchy of global and local planners is one of the most commonly u...

0 Kohei Honda, et al. ∙

∙ 04/18/2023

Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints

This study presents a benchmark for evaluating action-constrained reinfo...

0 Kazumi Kasaura, et al. ∙

∙ 03/02/2023

Risk-aware Path Planning via Probabilistic Fusion of Traversability Prediction for Planetary Rovers on Heterogeneous Terrains

Machine learning (ML) plays a crucial role in assessing traversability f...

0 Masafumi Endo, et al. ∙

∙ 01/26/2023

Periodic Multi-Agent Path Planning

Multi-agent path planning (MAPP) is the problem of planning collision-fr...

0 Kazumi Kasaura, et al. ∙

∙ 01/24/2022

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

Multi-agent path planning (MAPP) in continuous spaces is a challenging p...

2 Keisuke Okumura, et al. ∙

∙ 12/08/2021

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

We present ShinRL, an open-source library specialized for the evaluation...

17 Toshinori Kitamura, et al. ∙

∙ 09/16/2020

Path Planning using Neural A* Search

We present Neural A*, a novel data-driven search algorithm for path plan...

6 Ryo Yonetani, et al. ∙

∙ 08/18/2020

Adaptive Distillation for Decentralized Learning from Heterogeneous Clients

This paper addresses the problem of decentralized learning to achieve a ...

8 Jiaxin Ma, et al. ∙

∙ 03/20/2020

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

This work presents a deep reinforcement learning framework for interacti...

0 Mai Nishimura, et al. ∙

∙ 11/22/2019

Crowd Density Forecasting by Modeling Patch-based Dynamics

Forecasting human activities observed in videos is a long-standing chall...

42 Hiroaki Minoura, et al. ∙

∙ 09/28/2019

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

Transfer reinforcement learning (RL) aims at improving learning efficien...

6 Mohammadamin Barekatain, et al. ∙

∙ 05/23/2019

Decentralized Learning of Generative Adversarial Networks from Multi-Client Non-iid Data

This work addresses a new problem of learning generative adversarial net...

4 Ryo Yonetani, et al. ∙

∙ 05/17/2019

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

A decentralized learning mechanism, Federated Learning (FL), has attract...

0 Naoya Yoshida, et al. ∙

∙ 03/04/2019

Modeling Social Group Communication with Multi-Agent Imitation Learning

In crowded social scenarios with a myriad of external stimuli, human bra...

10 Navyata Sanghvi, et al. ∙

∙ 04/23/2018

Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

We envision a mobile edge computing (MEC) framework for machine learning...

0 Takayuki Nishio, et al. ∙

∙ 11/30/2017

Future Person Localization in First-Person Videos

We present a new task that predicts future locations of people observed ...

0 Takuma Yagi, et al. ∙

∙ 04/07/2017

Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption

We propose a privacy-preserving framework for learning visual classifier...

0 Ryo Yonetani, et al. ∙

∙ 06/15/2016

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

We envision a future time when wearable cameras are worn by the masses a...

0 Ryo Yonetani, et al. ∙

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