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TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20270314T030000 RDATE:20271107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20280312T030000 RDATE:20281105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20290311T030000 RDATE:20291104T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20300310T030000 RDATE:20301103T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20310309T030000 RDATE:20311102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20320314T030000 RDATE:20321107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20330313T030000 RDATE:20331106T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20340312T030000 RDATE:20341105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:cff074f57dd5d0e562126f46652ef042 CATEGORIES:Qualifying Exam CREATED:20190823T084024 SUMMARY:Physics-based Scene-level reasoning for Object Pose Estimation in Clutter LOCATION:1 Spring Street\, Room 403\, New Brunswick DESCRIPTION;ENCODING=QUOTED-PRINTABLE:
Abstract :
Progre ss has been achieved recently in object recognition given advancements in d eep learning. Nevertheless, such tools typically require a large amount of training data and significant manual effort to label objects. This limits t heir applicability in robotics, where solutions must scale to a large numbe r of objects and variety of conditions. Moreover, the combinatorial nature of the scenes that could arise from the placement of multiple objects is ha rd to capture in the training dataset. Thus, the learned models might not p roduce the desired level of precision required for tasks, such as robotic m anipulation. In this talk, I will present an autonomous process for pose es timation that spans from automated data generation to time-efficient scene- level reasoning and lifelong self-learning. In particular, the proposed fra mework first generates a labeled dataset for training a Convolutional Neura l Network (CNN) for object detection in clutter. These detections are used to guide a scene-level optimization process, which considers the interactio ns between the different objects present in the clutter to output pose esti mates of high precision. Furthermore, confident estimates are used to label online real images from multiple views and re-train the process in a lifel ong self-learning pipeline.
DTSTAMP:20240328T101616Z DTSTART;TZID=America/New_York:20181025T110000 SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR