{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/active-model-learning-and-diverse-action","title":"Active model learning and diverse action sampling for task and motion planning","arxiv_id":"1803.00967","date":"2018-03-02","proceeding":null,"authors":["Zi Wang","Caelan Reed Garrett","Leslie Pack Kaelbling","Tomás Lozano-Pérez"],"abstract":"The objective of this work is to augment the basic abilities of a robot by\nlearning to use new sensorimotor primitives to enable the solution of complex\nlong-horizon problems. Solving long-horizon problems in complex domains\nrequires flexible generative planning that can combine primitive abilities in\nnovel combinations to solve problems as they arise in the world. In order to\nplan to combine primitive actions, we must have models of the preconditions and\neffects of those actions: under what circumstances will executing this\nprimitive achieve some particular effect in the world?\n  We use, and develop novel improvements on, state-of-the-art methods for\nactive learning and sampling. We use Gaussian process methods for learning the\nconditions of operator effectiveness from small numbers of expensive training\nexamples collected by experimentation on a robot. We develop adaptive sampling\nmethods for generating diverse elements of continuous sets (such as robot\nconfigurations and object poses) during planning for solving a new task, so\nthat planning is as efficient as possible. We demonstrate these methods in an\nintegrated system, combining newly learned models with an efficient\ncontinuous-space robot task and motion planner to learn to solve long horizon\nproblems more efficiently than was previously possible.","url_abs":"http://arxiv.org/abs/1803.00967v2","url_pdf":"http://arxiv.org/pdf/1803.00967v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"active-model-learning-and-diverse-action","repo_url":"https://github.com/zi-w/Kitchen2D","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"active-model-learning-and-diverse-action","repo_url":"https://github.com/jingxixu/Kitchen2D---stir","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"motion-planning","task_name":"Motion Planning"},{"task_slug":"task-and-motion-planning","task_name":"Task and Motion Planning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.00967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.00967"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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