TEXterity – Tactile Extrinsic deXterity: Simultaneous Tactile Estimation and Control for Extrinsic Dexterity
CoRR(2024)
摘要
We introduce a novel approach that combines tactile estimation and control
for in-hand object manipulation. By integrating measurements from robot
kinematics and an image-based tactile sensor, our framework estimates and
tracks object pose while simultaneously generating motion plans in a receding
horizon fashion to control the pose of a grasped object. This approach consists
of a discrete pose estimator that tracks the most likely sequence of object
poses in a coarsely discretized grid, and a continuous pose
estimator-controller to refine the pose estimate and accurately manipulate the
pose of the grasped object. Our method is tested on diverse objects and
configurations, achieving desired manipulation objectives and outperforming
single-shot methods in estimation accuracy. The proposed approach holds
potential for tasks requiring precise manipulation and limited intrinsic
in-hand dexterity under visual occlusion, laying the foundation for closed-loop
behavior in applications such as regrasping, insertion, and tool use. Please
see https://sites.google.com/view/texterity for videos of real-world
demonstrations.
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