{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/143223"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/143223","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Automatic, Careful Online Packing of Groceries Using a Soft Robotic Manipulator and Multimodal Sensing","abstract":"This thesis describes the use of soft robotic manipulators with multimodal sensing for estimating the physical properties of unknown objects to enable sorting and packing. Although bin packing has been a key benchmark task for robotic manipulation, the community has mainly focused on the placement of rigid rectilinear objects within the container. We address this by presenting a soft robotic hand that uses a combination of vision, motor-based proprioception and soft tactile sensors to identify and pack a stream of unknown objects. We translate the ill-defined human conception of a “well-packed container” into metrics that match combinations of our different sensor modalities and demonstrate how this works in a grocery packing scenario, where objects of arbitrary shape, size and stiffness come down a conveyor belt. The proposed multimodal approach is supported by physical experiments demonstrating how the integration of multiple sensing modalities can address complex manipulation applications.","abstract_html":"This thesis describes the use of soft robotic manipulators with multimodal sensing for estimating the physical properties of unknown objects to enable sorting and packing. Although bin packing has been a key benchmark task for robotic manipulation, the community has mainly focused on the placement of rigid rectilinear objects within the container. We address this by presenting a soft robotic hand that uses a combination of vision, motor-based proprioception and soft tactile sensors to identify and pack a stream of unknown objects. We translate the ill-defined human conception of a “well-packed container” into metrics that match combinations of our different sensor modalities and demonstrate how this works in a grocery packing scenario, where objects of arbitrary shape, size and stiffness come down a conveyor belt. The proposed multimodal approach is supported by physical experiments demonstrating how the integration of multiple sensing modalities can address complex manipulation applications.","abstract_has_math":false,"creators":["Choi, Jeana"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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Although bin packing has been a key benchmark task for robotic manipulation, the community has mainly focused on the placement of rigid rectilinear objects within the container. We address this by presenting a soft robotic hand that uses a combination of vision, motor-based proprioception and soft tactile sensors to identify and pack a stream of unknown objects. We translate the ill-defined human conception of a “well-packed container” into metrics that match combinations of our different sensor modalities and demonstrate how this works in a grocery packing scenario, where objects of arbitrary shape, size and stiffness come down a conveyor belt. 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Although bin packing has been a key benchmark task for robotic manipulation, the community has mainly focused on the placement of rigid rectilinear objects within the container. We address this by presenting a soft robotic hand that uses a combination of vision, motor-based proprioception and soft tactile sensors to identify and pack a stream of unknown objects. We translate the ill-defined human conception of a “well-packed container” into metrics that match combinations of our different sensor modalities and demonstrate how this works in a grocery packing scenario, where objects of arbitrary shape, size and stiffness come down a conveyor belt. 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