4 papers
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…
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…
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…