13 papers
CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding
Yuri Ishitoya, Jeremy Siburian, Masashi Hamaya +3
Vision-language-action models (VLAs) inherit semantic capabilities from pretrained VLMs, yet large-scale post-training on robot data and architectural modifications can reshape the…
Robust and Resilient Soft Robotic Object Insertion with Compliance-Enabled Contact Formation and Failure Recovery
Mimo Shirasaka, Cristian C. Beltran-Hernandez, Masashi Hamaya +1
Object insertion tasks are prone to failure under pose uncertainty and environmental variation, often requiring manual fine-tuning or controller retraining. We present a novel appr…
SCU-Hand with Integrated Single-Sheet Valve: A Funnel-Shaped Robotic Hand for Milligram-Scale Powder Handling
Tomoya Takahashi, Yusaku Nakajima, Cristian Camilo Beltran-Hernandez +5
Laboratory Automation (LA) has the potential to accelerate solid-state materials discovery by enabling continuous robotic operation without human intervention. While robotic system…
Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning
Koki Yamane, Cristian C. Beltran-Hernandez, Steven Oh +2
Fast execution of contact-rich manipulation is critical for practical deployment, yet providing fast demonstrations for imitation learning (IL) remains challenging: humans cannot d…
Simulation-Driven Evolutionary Motion Parameterization for Contact-Rich Granular Scooping with a Soft Conical Robotic Hand
Yongliang Wang, Cristian C. Beltran-Hernandez, Tomoya Takahashi +1
Tool-based scooping is vital in robot-assisted tasks, enabling interaction with objects of varying sizes, shapes, and material states. Recent studies have shown that flexible, reco…
A Soft Wrist with Anisotropic and Selectable Stiffness for Robust Robot Learning in Contact-rich Manipulation
Steven Oh, Tomoya Takahashi, Cristian C. Beltran-Hernandez +2
Contact-rich manipulation tasks in unstructured environments pose significant robustness challenges for robot learning, where unexpected collisions can cause damage and hinder poli…