5 papers
See to Reach, Feel to Grasp: Learning A Blind Grasp Reflex for Anthropomorphic Robotic Hands
Alexander Alexiev, Tzu-Yuan Lin, Sang Min Kim +3
In this work we study if a robotic hand using proprioception alone can grasp diverse objects with no visual observation. We present a modular dexterous grasping architecture that s…
Real-Time Force Regulation for Whole-Hand Dexterous Grasping
Sang Min Kim, Alexander Alexiev, Tzu-Yuan Lin +3
Robust dexterous grasping requires maintaining physical stability despite contacts interactively evolving across the entire hand. A precomputed force distribution can easily fail u…
Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control
Ho Jae Lee, Yonghyeon Lee, Alexander Alexiev +3
In this work, we propose a hybrid hierarchical control framework for reactive dexterous grasping that explicitly decouples high-level spatial intent from low-level joint execution.…
High-Bandwidth Tactile-Reactive Control for Grasp Adjustment
Yonghyeon Lee, Tzu-Yuan Lin, Alexander Alexiev +1
Vision-only grasping systems are fundamentally constrained by calibration errors, sensor noise, and grasp pose prediction inaccuracies, leading to unavoidable contact uncertainty i…
Hierarchical Reactive Grasping via Task-Space Velocity Fields and Joint-Space Quadratic Programming
Yonghyeon Lee, Tzu-Yuan Lin, Alexander Alexiev +1
We present a fast and reactive grasping framework that combines task-space velocity fields with joint-space Quadratic Program (QP) in a hierarchical structure. Reactive, collision-…