BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20240926T014018Z UID:C8421993-446C-40C4-8BFC-9C070B450C22 DTSTART;TZID=America/Los_Angeles:20240925T173000 DTEND;TZID=America/Los_Angeles:20240925T183000 DESCRIPTION:The human fascination to mimic ultra-efficient living beings li ke insects and birds has led to the rise of small autonomous robots. Small er robots are safer\, more agile and are task distributable as swarms. One might wonder\, why do we not have small robots deployed in the wild today ? Smaller robots are constrained by a severe dearth of computation and sen sor quality. To further exacerbate the situation\, today's mainstream appr oach for autonomy on small robots relies on building a 3D map of the scene that is used to plan paths for executing a control algorithm. Such a meth odology has severely bounded the potential of small autonomous robots due to the strict distinction between perception\, planning\, and control. Ins tead\, we re-imagine each agent by drawing inspiration from insects at the bottom of the size and computation spectrum. Specifically\, each of our a gents comprises a series of hierarchical competencies built on bio-inspire d sensorimotor AI loops by utilizing the action-perception synergy. Here\, the agent controls its movement and physical interaction to make up for i ts lack of computation and sensing. Such an approach imposes additional co nstraints on the data gathered to solve the problem using Active and Inter active Perception. To unify the class of motion problems\, we present a me thod to exploit the unknown\, i.e.\, the uncertainty of the predictions to obtain additional informational cues in a new theory called Novel Percept ion that utilizes the statistics of motion fields to tackle various classe s of problems from navigation and interaction. This method has the potenti al to be the go-to mathematical formulation for tackling the class of moti on-field-based problems in robotics and made it into the cover of Science Robotics journal.\n\nCo-sponsored by: Media Partner: Open Research Institu te (ORI)\n\nSpeaker(s): Nitin J. Sanket\n\nAgenda: \n- Invited talk from P rof.[Nitin Sanket](https://www.wpi.edu/people/faculty/nitin)\, from Roboti cs Engineering department at Worcester Polytechnic Institute.\n- Q/A Sessi on\n\nVirtual: https://events.vtools.ieee.org/m/433949 LOCATION:Virtual: https://events.vtools.ieee.org/m/433949 ORGANIZER:upalmahbub@yahoo.com SEQUENCE:30 SUMMARY:From Mystery to Mastery: Tiny Robot Autonomy using the Manifestatio n of the Unknown URL;VALUE=URI:https://events.vtools.ieee.org/m/433949 X-ALT-DESC:Description: <br /><p>The human fascination to mimic ultra-effic ient living beings like insects and birds has led to the rise of small aut onomous robots. Smaller robots are safer\, more agile and are task distrib utable as swarms. One might wonder\, why do we not have small robots deplo yed in the wild today? Smaller robots are constrained by a severe dearth o f computation and sensor quality. To further exacerbate the situation\, to day's mainstream approach for autonomy on small robots relies on building a 3D map of the scene that is used to plan paths for executing a control a lgorithm. Such a methodology has severely bounded the potential of small a utonomous robots due to the strict distinction between perception\, planni ng\, and control. Instead\, we re-imagine each agent by drawing inspiratio n from insects at the bottom of the size and computation spectrum. Specifi cally\, each of our agents comprises a series of hierarchical competencies built on bio-inspired sensorimotor AI loops by utilizing the action-perce ption synergy. Here\, the agent controls its movement and physical interac tion to make up for its lack of computation and sensing. Such an approach imposes additional constraints on the data gathered to solve the problem u sing Active and Interactive Perception. To unify the class of motion probl ems\, we present a method to exploit the unknown\, i.e.\, the uncertainty of the predictions to obtain additional informational cues in a new theory called Novel Perception that utilizes the statistics of motion fields to tackle various classes of problems from navigation and interaction. This m ethod has the potential to be the go-to mathematical formulation for tackl ing the class of motion-field-based problems in robotics and made it into the cover of Science Robotics journal.&nbsp\;</p><br /><br />Agenda: <br / ><ul>\n<li>Invited talk from Prof.<a style="display: inline !important\;" href="https://www.wpi.edu/people/faculty/nitin">\n<h3 class="no-margin" st yle="display: inline !important\;">Nitin Sanket</h3>\n</a>\, from Robotics Engineering department at Worcester Polytechnic Institute.&nbsp\;</li>\n< li>Q/A Session</li>\n</ul> END:VEVENT END:VCALENDAR