Stable and Semantic Robotic Grasping Using Tactile Feedback

Hao Dang

Stable and Semantic Robotic Grasping Using Tactile Feedback
Dang, Hao
Computer Science
Ph.D., Columbia University.
This thesis covers two topics of robotic grasping: stable grasping and semantic grasping. The first part of the thesis is dedicated to the stable grasping problem, where we focus on a grasping pipeline that robustly executes a planned-to-be stable grasp under uncertainty. To this end, we first present a learning method which estimates the stability of a grasp based on tactile feedback and hand kinematic data. We then show our hand adjustment algorithm which works with the grasp stability estimator and synthesizes hand adjustments to optimize a grasp towards a stable one. With these two methods, we obtain a grasping pipeline with a closed-loop grasp adjustment process which increases the grasping performance under uncertainty. The second part of the thesis considers how robotic grasping should be accomplished to facilitate a manipulation task that follows the grasp. Certain task-related constraints should be satisfied by the grasp in use, which we refer to as semantic constraints. We first develop an example-based method to encode semantic constraints and to plan stable grasps according to the encoded semantic constraints. We then design a task description framework to abstract an object manipulation task. Within this framework, we also present a method which could automatically construct this manipulation task abstraction from a human demonstration.
Computer science
Artificial intelligence
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