11 papers
Evaluating Tactile Foundation Models for End-to-End Imitation Learning in Contact-Rich Manipulation
Masaki Murooka, Ryoichi Nakajo, Tomohiro Motoda +4
Tactile sensing plays an important role in imitation learning for contact-rich manipulation by providing direct observations of physical contact with objects. Recently, several tac…
TOAST: Stochastic Robot Action Tokenization for Autoregressive Vision-Language-Action Models
Keisuke Shirai, Tomohiro Motoda, Hanbit Oh +5
Autoregressive Vision-Language-Action models often represent continuous robot actions as discrete token sequences, enabling action prediction with standard next-token objectives. F…
Self-Supervised Anchoring of Fingertip Sensing to Proprioception and Proactive Actions for Robot Imitation Learning
Tomohiro Motoda, Masaki Murooka, Keisuke Shirai +6
Robotic imitation learning often relies on external cameras, yet local interaction cues such as object proximity, contact onset, and grasp state are difficult to observe near the f…
Improving Imitation Learning Efficiency for Manipulation through Geometric Prior Pretraining
Shogo Iwakata, Tomohiro Motoda, Ryosuke Yamada +8
Applying an imitation learning policy to a new manipulation task usually requires collecting new demonstrations and retraining the model, which makes sample efficiency a practical…
Non-Prehensile Throwing: A Reinforcement Learning Perspective
Abdullah Mustafa, Ryo Hanai, Ixchel G. Ramirez-Alpizar +4
Robotic throwing enables fast object transport and extends a robot's reachable workspace beyond traditional pick-and-place. While prehensile (grasp-based) throwing works well for g…
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…
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…
Visual Imitation Learning of Non-Prehensile Manipulation Tasks with Dynamics-Supervised Models
Abdullah Mustafa, Ryo Hanai, Ixchel Ramirez +4
Unlike quasi-static robotic manipulation tasks like pick-and-place, dynamic tasks such as non-prehensile manipulation pose greater challenges, especially for vision-based control.…