{"id":{"repo_id":"columbia-diss","oai_identifier":"oai:academiccommons.columbia.edu:10.7916/D81Z4BN5"},"canonical_url":"https://search.dev.ndltd.org/etd/columbia-diss/oai:academiccommons.columbia.edu:10.7916/D81Z4BN5","repository":{"repo_id":"columbia-diss","name":"Columbia University","base_url":"https://academiccommons.columbia.edu/oai"},"display":{"title":"Stable and Semantic Robotic Grasping Using Tactile Feedback","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Dang, Hao"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013","date_published":"2013","updated_at":"2026-07-24T01:44:27Z","subjects":["Robotics","Computer science","Artificial intelligence"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.7916/D81Z4BN5","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Dang, Hao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013"]},{"key":"dc:type","label":"Dc Type","values":["Theses"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Robotics","Computer science","Artificial intelligence"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.7916/D81Z4BN5"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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."]},{"key":"dc:title","label":"Title","values":["Stable and Semantic Robotic Grasping Using Tactile Feedback"]}]}],"canonical_facts":{"dc:creator":["Dang, Hao"],"dc:date":["2013"],"dc:description":["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."],"dc:identifier":["https://doi.org/10.7916/D81Z4BN5"],"dc:language":["English"],"dc:subject":["Robotics","Computer science","Artificial intelligence"],"dc:title":["Stable and Semantic Robotic Grasping Using Tactile Feedback"],"dc:type":["Theses"]},"updated_at":"2026-07-24T01:44:27Z"}