103 citations · 104 across the 16 of their papers we have counts for
3 papers · 1 filter
Learning Hierarchical Skill Policies with Offline Quality-Diversity Reinforcement Learning
Tanachai Anakewat, Takayuki Osa, Tatsuya Harada
Recent studies investigate how to leverage pre-collected datasets to improve the policy performance and sample efficiency of RL. One promising approach to achieve this goal is to e…
Self-Supervised Theorem Discovery in a Formal Axiomatic System
Kazuki Ota, Takayuki Osa, Tatsuya Harada
Recent artificial intelligence (AI) systems have shown remarkable progress in mathematical reasoning. Many existing approaches, including large language models (LLMs), draw on huma…
Cross-Embodiment Offline Reinforcement Learning for Heterogeneous Robot Datasets
Haruki Abe, Takayuki Osa, Yusuke Mukuta +1
Scalable robot policy pre-training has been hindered by the high cost of collecting high-quality demonstrations for each platform. In this study, we address this issue by uniting o…