24 citations · 26 across the 6 of their papers we have counts for
7 papers
Where-to-Unmask: Ground-Truth-Guided Unmasking Order Learning for Masked Diffusion Language Models
Hikaru Asano, Tadashi Kozuno, Kuniaki Saito +1
Masked Diffusion Language Models (MDLMs) generate text by iteratively filling masked tokens, requiring two coupled decisions at each step: which positions to unmask (where-to-unmas…
Symmetry-Breaking in Multi-Agent Navigation: Winding Number-Aware MPC with a Learned Topological Strategy
Tomoki Nakao, Kazumi Kasaura, Tadashi Kozuno
In decentralized multi-agent navigation, agents that independently compute their controls without communicating goals or intentions can fall into symmetry-induced deadlocks because…
Self Iterative Label Refinement via Robust Unlabeled Learning
Hikaru Asano, Tadashi Kozuno, Yukino Baba
Recent advances in large language models (LLMs) have yielded impressive performance on various tasks, yet they often depend on high-quality feedback that can be costly. Self-refine…
Multi-Agent Behavior Retrieval: Retrieval-Augmented Policy Training for Cooperative Push Manipulation by Mobile Robots
So Kuroki, Mai Nishimura, Tadashi Kozuno
Due to the complex interactions between agents, learning multi-agent control policy often requires a prohibited amount of data. This paper aims to enable multi-agent systems to eff…
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…