{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/123769"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/123769","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Inference and learning for rigid-body models of manipulation","abstract":"In this thesis, we explore a spectrum of inference and modeling approaches for robotic manipulation. Particularly, we investigate the broad class of rigid-bodies undergoing frictional interactions. We begin by deriving a contact-implicit system identification formulation for articulated rigid-bodies. Assuming we have a physical model of the system, the objective is to derive system parameters and contact forces for articulated rigid-bodies without enumerating and inferring contact formations. We then ground this approach by investigating the fidelity of rigid-body contact models and their identification. We evaluate the fidelity of the contact models by empirically studying their predictive performance and parameter identification properties in a planar impact task. Next, we address one approach to augmenting these contact models with data. The objective here is to improve model fidelity through an optimization of model parameters and residual error learning for systems with prior physics models. We conclude the thesis by building models from data for tasks with rich latent structure and no prior physics models. Here, the objective is to learn data-efficient hierarchical models of physics that incorporate force and tactile sensory modalities and are amenable to inference, controls, and planning.","abstract_html":"In this thesis, we explore a spectrum of inference and modeling approaches for robotic manipulation. Particularly, we investigate the broad class of rigid-bodies undergoing frictional interactions. We begin by deriving a contact-implicit system identification formulation for articulated rigid-bodies. Assuming we have a physical model of the system, the objective is to derive system parameters and contact forces for articulated rigid-bodies without enumerating and inferring contact formations. We then ground this approach by investigating the fidelity of rigid-body contact models and their identification. We evaluate the fidelity of the contact models by empirically studying their predictive performance and parameter identification properties in a planar impact task. Next, we address one approach to augmenting these contact models with data. The objective here is to improve model fidelity through an optimization of model parameters and residual error learning for systems with prior physics models. We conclude the thesis by building models from data for tasks with rich latent structure and no prior physics models. Here, the objective is to learn data-efficient hierarchical models of physics that incorporate force and tactile sensory modalities and are amenable to inference, controls, and planning.","abstract_has_math":false,"creators":["Fazeli, Nima."],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Mechanical Engineering","school":null,"contributors":[],"advisors":["Alberto Rodriguez."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:21:15Z","subjects":["Mechanical Engineering."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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We begin by deriving a contact-implicit system identification formulation for articulated rigid-bodies. Assuming we have a physical model of the system, the objective is to derive system parameters and contact forces for articulated rigid-bodies without enumerating and inferring contact formations. We then ground this approach by investigating the fidelity of rigid-body contact models and their identification. We evaluate the fidelity of the contact models by empirically studying their predictive performance and parameter identification properties in a planar impact task. Next, we address one approach to augmenting these contact models with data. The objective here is to improve model fidelity through an optimization of model parameters and residual error learning for systems with prior physics models. We conclude the thesis by building models from data for tasks with rich latent structure and no prior physics models. Here, the objective is to learn data-efficient hierarchical models of physics that incorporate force and tactile sensory modalities and are amenable to inference, controls, and planning."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph. D."]},{"key":"dc:title","label":"Title","values":["Inference and learning for rigid-body models of manipulation"]}]}],"canonical_facts":{"dc:contributor.advisor":["Alberto Rodriguez."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Mechanical Engineering","MechE"],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Mechanical Engineering."],"dc:creator":["Fazeli, Nima."],"dc:date.accessioned":["2020-02-10T21:43:18Z"],"dc:date.available":["2020-02-10T21:43:18Z"],"dc:date.issued":["2019"],"dc:description":["Thesis: Ph. 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Next, we address one approach to augmenting these contact models with data. The objective here is to improve model fidelity through an optimization of model parameters and residual error learning for systems with prior physics models. We conclude the thesis by building models from data for tasks with rich latent structure and no prior physics models. Here, the objective is to learn data-efficient hierarchical models of physics that incorporate force and tactile sensory modalities and are amenable to inference, controls, and planning."],"dc:description.degree":["Ph. D."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/123769"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Mechanical Engineering."],"dc:title":["Inference and learning for rigid-body models of manipulation"],"dc:type":["Thesis"],"thesis:degree_name":["Doctoral"]},"updated_at":"2026-07-22T22:21:15Z"}