{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/150293"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/150293","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Active Predicate Learning","abstract":"Planning in robotics environments is difficult in part due to continuous state and action spaces. One approach to this challenge is to use bilevel planning, where decisionmaking occurs in multiple levels of abstraction. However, the efficacy and efficiency of bilevel planning relies on the underlying set of state and action abstractions. It is impractical to assume these abstractions as given (i.e. hand-designed by humans), so instead the agent should learn them, for example by exploring and interacting with its environment under the guidance of a teacher, from whom the robot may query expert knowledge. This is more difficult than the typical active learning problem setting because the robot must take actions to get to a state before it can make useful queries of the teacher about that state. This work develops an active learning framework for learning state abstractions (predicates) for effective and efficient task and motion planning. Given the names and arguments of the predicates in an environment and very few pre-labeled examples, the agent is able to learn predicate classifiers that enable it to successfully complete test tasks.","abstract_html":"Planning in robotics environments is difficult in part due to continuous state and action spaces. One approach to this challenge is to use bilevel planning, where decisionmaking occurs in multiple levels of abstraction. However, the efficacy and efficiency of bilevel planning relies on the underlying set of state and action abstractions. It is impractical to assume these abstractions as given (i.e. hand-designed by humans), so instead the agent should learn them, for example by exploring and interacting with its environment under the guidance of a teacher, from whom the robot may query expert knowledge. This is more difficult than the typical active learning problem setting because the robot must take actions to get to a state before it can make useful queries of the teacher about that state. This work develops an active learning framework for learning state abstractions (predicates) for effective and efficient task and motion planning. Given the names and arguments of the predicates in an environment and very few pre-labeled examples, the agent is able to learn predicate classifiers that enable it to successfully complete test tasks.","abstract_has_math":false,"creators":["Li, Amber"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Kaelbling, Leslie","Silver, Tom"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-02","date_published":"2023-02","updated_at":"2026-07-22T22:21:04Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"rights_urls":["http://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/150293","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kaelbling, Leslie","Silver, Tom"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Li, Amber"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-03-31T14:45:39Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-03-31T14:45:39Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-02"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Engineering in Electrical Engineering and Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright MIT"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/150293"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Planning in robotics environments is difficult in part due to continuous state and action spaces. One approach to this challenge is to use bilevel planning, where decisionmaking occurs in multiple levels of abstraction. However, the efficacy and efficiency of bilevel planning relies on the underlying set of state and action abstractions. It is impractical to assume these abstractions as given (i.e. hand-designed by humans), so instead the agent should learn them, for example by exploring and interacting with its environment under the guidance of a teacher, from whom the robot may query expert knowledge. This is more difficult than the typical active learning problem setting because the robot must take actions to get to a state before it can make useful queries of the teacher about that state. This work develops an active learning framework for learning state abstractions (predicates) for effective and efficient task and motion planning. Given the names and arguments of the predicates in an environment and very few pre-labeled examples, the agent is able to learn predicate classifiers that enable it to successfully complete test tasks."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Active Predicate Learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kaelbling, Leslie","Silver, Tom"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Li, Amber"],"dc:date.accessioned":["2023-03-31T14:45:39Z"],"dc:date.available":["2023-03-31T14:45:39Z"],"dc:date.issued":["2023-02"],"dc:description.abstract":["Planning in robotics environments is difficult in part due to continuous state and action spaces. One approach to this challenge is to use bilevel planning, where decisionmaking occurs in multiple levels of abstraction. However, the efficacy and efficiency of bilevel planning relies on the underlying set of state and action abstractions. It is impractical to assume these abstractions as given (i.e. hand-designed by humans), so instead the agent should learn them, for example by exploring and interacting with its environment under the guidance of a teacher, from whom the robot may query expert knowledge. This is more difficult than the typical active learning problem setting because the robot must take actions to get to a state before it can make useful queries of the teacher about that state. This work develops an active learning framework for learning state abstractions (predicates) for effective and efficient task and motion planning. Given the names and arguments of the predicates in an environment and very few pre-labeled examples, the agent is able to learn predicate classifiers that enable it to successfully complete test tasks."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/150293"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Active Predicate Learning"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:04Z"}