4 citations · 9 across the 3 of their papers we have counts for
9 papers
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…
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…