6 papers
The Embodiment Gap in Robot Foundation Models
Yukiyasu Domae, Keisuke Shirai, Hanbit Oh +7
Robot foundation models (RFMs), including vision-language-action (VLA) policies, are often discussed through a scaling view: more data, larger models, and broader benchmarks should…
TWINS: A Tactile Wearable Isomorphic Arm Networked System for Contact-Rich Manipulation Learning
Takahide Kitamura, Masaki Murooka, Natsuki Yamanobe +1
Recent advances in robot learning for manipulation have increased the importance of collecting real-world demonstration data. However, existing robotic systems primarily focus on e…
GuidedAttention: Interpretable and Correctable Visual Attention for OOD-Robust Robot Manipulation via Imitation Learning
Masaki Murooka, Ryoichi Nakajo, Keisuke Shirai +4
End-to-end visuomotor policies provide little opportunity for humans to understand or correct the policy's visual attention. We propose GuidedAttention, a visuomotor imitation lear…
RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments
Masaki Murooka, Tomohiro Motoda, Ryoichi Nakajo +5
We present RoboManipBaselines, an open-source software framework for imitation learning research in robotic manipulation. The framework supports the entire imitation learning pipel…
Self-Augmented Robot Trajectory: Efficient Imitation Learning via Safe Self-augmentation with Demonstrator-annotated Precision
Hanbit Oh, Masaki Murooka, Tomohiro Motoda +2
Imitation learning is a promising paradigm for training robot agents; however, standard approaches typically require substantial data acquisition -- via numerous demonstrations or…
Learning Bimanual Manipulation via Action Chunking and Inter-Arm Coordination with Transformers
Tomohiro Motoda, Ryo Hanai, Ryoichi Nakajo +3
Robots that can operate autonomously in a human living environment are necessary to have the ability to handle various tasks flexibly. One crucial element is coordinated bimanual m…