RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulated Environments
Masaki Murooka, Tomohiro Motoda, Ryoichi Nakajo, Hanbit Oh, Koshi Makihara, Keisuke Shirai, Yukiyasu Domae
Published: 2025/9/21
Abstract
RoboManipBaselines is an open framework for robot imitation learning that unifies data collection, training, and evaluation across simulation and real robots. We introduce it as a platform enabling systematic benchmarking of diverse tasks, robots, and multimodal policies with emphasis on integration, generality, extensibility, and reproducibility.