1.6k citations · 1.7k across the 17 of their papers we have counts for
19 papers
Egocentric Action-aware Inertial Localization in Point Clouds with Vision-Language Guidance
Mingfang Zhang, Ryo Yonetani, Yifei Huang +3
This paper presents a novel inertial localization framework named Egocentric Action-aware Inertial Localization (EAIL), which leverages egocentric action cues from head-mounted IMU…
Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints
Kazumi Kasaura, Shuwa Miura, Tadashi Kozuno +3
This study presents a benchmark for evaluating action-constrained reinforcement learning (RL) algorithms. In action-constrained RL, each action taken by the learning system must co…
Counterfactual Fairness Filter for Fair-Delay Multi-Robot Navigation
Hikaru Asano, Ryo Yonetani, Mai Nishimura +1
Multi-robot navigation is the task of finding trajectories for a team of robotic agents to reach their destinations as quickly as possible without collisions. In this work, we intr…
When to Replan? An Adaptive Replanning Strategy for Autonomous Navigation using Deep Reinforcement Learning
Kohei Honda, Ryo Yonetani, Mai Nishimura +1
The hierarchy of global and local planners is one of the most commonly utilized system designs in autonomous robot navigation. While the global planner generates a reference path f…
Risk-aware Path Planning via Probabilistic Fusion of Traversability Prediction for Planetary Rovers on Heterogeneous Terrains
Masafumi Endo, Tatsunori Taniai, Ryo Yonetani +1
Machine learning (ML) plays a crucial role in assessing traversability for autonomous rover operations on deformable terrains but suffers from inevitable prediction errors. Especia…
Periodic Multi-Agent Path Planning
Kazumi Kasaura, Ryo Yonetani, Mai Nishimura
Multi-agent path planning (MAPP) is the problem of planning collision-free trajectories from start to goal locations for a team of agents. This work explores a relatively unexplore…
CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces
Keisuke Okumura, Ryo Yonetani, Mai Nishimura +1
Multi-agent path planning (MAPP) in continuous spaces is a challenging problem with significant practical importance. One promising approach is to first construct graphs approximat…
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives
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. Existing RL lib…
Path Planning using Neural A* Search
Ryo Yonetani, Tatsunori Taniai, Mohammadamin Barekatain +2
We present Neural A*, a novel data-driven search method for path planning problems. Despite the recent increasing attention to data-driven path planning, machine learning approache…
Adaptive Distillation for Decentralized Learning from Heterogeneous Clients
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…
L2B: Learning to Balance the Safety-Efficiency Trade-off in Interactive Crowd-aware Robot Navigation
Mai Nishimura, Ryo Yonetani
This work presents a deep reinforcement learning framework for interactive navigation in a crowded place. Our proposed approach, Learning to Balance (L2B) framework enables mobile…
Crowd Density Forecasting by Modeling Patch-based Dynamics
Hiroaki Minoura, Ryo Yonetani, Mai Nishimura +1
Forecasting human activities observed in videos is a long-standing challenge in computer vision, which leads to various real-world applications such as mobile robots, autonomous dr…
MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics
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. However, it r…
Decentralized Learning of Generative Adversarial Networks from Non-iid Data
Ryo Yonetani, Tomohiro Takahashi, Atsushi Hashimoto +1
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) d…
Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data
Naoya Yoshida, Takayuki Nishio, Masahiro Morikura +2
This paper proposes a cooperative mechanism for mitigating the performance degradation due to non-independent-and-identically-distributed (non-IID) data in collaborative machine le…
MGpi: A Computational Model of Multiagent Group Perception and Interaction
Navyata Sanghvi, Ryo Yonetani, Kris Kitani
Toward enabling next-generation robots capable of socially intelligent interaction with humans, we present a of interactions in a social environmen…
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
Takayuki Nishio, Ryo Yonetani
We envision a mobile edge computing (MEC) framework for machine learning (ML) technologies, which leverages distributed client data and computation resources for training high-perf…
Future Person Localization in First-Person Videos
Takuma Yagi, Karttikeya Mangalam, Ryo Yonetani +1
We present a new task that predicts future locations of people observed in first-person videos. Consider a first-person video stream continuously recorded by a wearable camera. Giv…
Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption
Ryo Yonetani, Vishnu Naresh Boddeti, Kris M. Kitani +1
We propose a privacy-preserving framework for learning visual classifiers by leveraging distributed private image data. This framework is designed to aggregate multiple classifiers…