10 papers · 1 filter
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…
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…
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…
A Flexible Field-Based Policy Learning Framework for Diverse Robotic Systems and Sensors
Jose Gustavo Buenaventura Carreon, Floris Erich, Roman Mykhailyshyn +3
We present a cross robot visuomotor learning framework that integrates diffusion policy based control with 3D semantic scene representations from D3Fields to enable category level…
Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT
Muhammad A. Muttaqien, Tomohiro Motoda, Ryo Hanai +1
Robotic pick-and-place tasks in convenience stores pose challenges due to dense object arrangements, occlusions, and variations in object properties such as color, shape, size, and…