6 papers · 1 filter
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…
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…
ORPA: Online Residual Policy Adaptation for Robot Manipulation Control with Human Feedback
Muhammad A. Muttaqien, Tomohiro Motoda, Ryo Hanai +1
Robotic manipulation policies trained via imitation learning, such as Action Chunking with Transformers (ACT), can achieve strong performance under ideal conditions but often remai…
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…
Learning to Predict Contact Force Distributions from Vision Leveraging Object Geometry Priors
Ryo Hanai, Yukiyasu Domaea, Ixchel G. Ramirez-Alpizar +3
Based on vision and prior experience, humans can make rough physical predictions and adjust their manipulation strategies. This paper aims to endow robots with a similar ability. T…
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…