{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/163564"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/163564","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Planning for Dynamic Nonprehensile Object Transport","abstract":"Generalized planning methods for dynamic manipulation struggle to efficiently solve kinodynamic constraints. Gradient-based methods suffer from initialization sensitivity, local optimum convergence, and lack of feasibility guarantees, while sampling-based methods can require large computation times if there exist challenging boundary conditions. Iterative Time Optimal Path Parameterization, or iTOPP, guarantees a feasible local minimum for a dynamic grasping problem by iteratively decreasing transit time for a trajectory initially generated to satisfy kinodynamic contact constraints. We demonstrate solutions that can handle initial or final goal states defined as quasistatically infeasible, in which purely quasistatic motions cannot generate a warm start trajectory. We also design an indirect adaptive controller that can track a desired dynamic grasping trajectory assuming unknown object mass and location parameters.","abstract_html":"Generalized planning methods for dynamic manipulation struggle to efficiently solve kinodynamic constraints. Gradient-based methods suffer from initialization sensitivity, local optimum convergence, and lack of feasibility guarantees, while sampling-based methods can require large computation times if there exist challenging boundary conditions. Iterative Time Optimal Path Parameterization, or iTOPP, guarantees a feasible local minimum for a dynamic grasping problem by iteratively decreasing transit time for a trajectory initially generated to satisfy kinodynamic contact constraints. We demonstrate solutions that can handle initial or final goal states defined as quasistatically infeasible, in which purely quasistatic motions cannot generate a warm start trajectory. We also design an indirect adaptive controller that can track a desired dynamic grasping trajectory assuming unknown object mass and location parameters.","abstract_has_math":false,"creators":["Wang, Eric K."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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Gradient-based methods suffer from initialization sensitivity, local optimum convergence, and lack of feasibility guarantees, while sampling-based methods can require large computation times if there exist challenging boundary conditions. Iterative Time Optimal Path Parameterization, or iTOPP, guarantees a feasible local minimum for a dynamic grasping problem by iteratively decreasing transit time for a trajectory initially generated to satisfy kinodynamic contact constraints. We demonstrate solutions that can handle initial or final goal states defined as quasistatically infeasible, in which purely quasistatic motions cannot generate a warm start trajectory. We also design an indirect adaptive controller that can track a desired dynamic grasping trajectory assuming unknown object mass and location parameters."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Planning for Dynamic Nonprehensile Object Transport"]}]}],"canonical_facts":{"dc:contributor.advisor":["Leonard, John"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Mechanical Engineering"],"dc:creator":["Wang, Eric K."],"dc:date.accessioned":["2025-11-05T19:34:59Z"],"dc:date.available":["2025-11-05T19:34:59Z"],"dc:date.issued":["2025-05"],"dc:description.abstract":["Generalized planning methods for dynamic manipulation struggle to efficiently solve kinodynamic constraints. Gradient-based methods suffer from initialization sensitivity, local optimum convergence, and lack of feasibility guarantees, while sampling-based methods can require large computation times if there exist challenging boundary conditions. Iterative Time Optimal Path Parameterization, or iTOPP, guarantees a feasible local minimum for a dynamic grasping problem by iteratively decreasing transit time for a trajectory initially generated to satisfy kinodynamic contact constraints. We demonstrate solutions that can handle initial or final goal states defined as quasistatically infeasible, in which purely quasistatic motions cannot generate a warm start trajectory. We also design an indirect adaptive controller that can track a desired dynamic grasping trajectory assuming unknown object mass and location parameters."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/163564"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","Copyright retained by author(s)"],"dc:rights.uri":["https://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:title":["Planning for Dynamic Nonprehensile Object Transport"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Mechanical Engineering"]},"updated_at":"2026-07-22T22:21:31Z"}