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PRODID:IEEE vTools.Events//EN
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TZID:Asia/Kolkata
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DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
TZNAME:IST
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BEGIN:VEVENT
DTSTAMP:20241026T081155Z
UID:CF7C8C6C-C3C1-456D-B7E6-99BB87B3B452
DTSTART;TZID=Asia/Kolkata:20241024T190000
DTEND;TZID=Asia/Kolkata:20241024T200000
DESCRIPTION:Interpreting visual signals introduces both challenges and oppo
 rtunities in the design of control and autonomous systems. This talk will 
 explore two key concepts that address these challenges. In the first part\
 , I will introduceperception contracts—an innovative approach to analyzi
 ng visual control systems that rely on Deep Neural Networks for state esti
 mation. A perception contract provides an over-approximation of a state es
 timator while guaranteeing closed-loop system invariants. These contracts 
 can be automatically synthesized using data and model-based analysis and h
 ave been successfully applied to systems such as automated landing control
 lers and lane-keeping systems. The second part of the talk will focus on a
 lgorithms for computing indistinguishable sets—sets of states that canno
 t be distinguished based on available visual data. These sets help define 
 the theoretical limits of visual control\, revealing the boundaries of wha
 t can be achieved with coarse measurements in dynamic environments. Throug
 hout the talk\, I will mention various examples\, highlight the tools avai
 lable\, and discuss open problems that invite further exploration in this 
 area.\n\nSpeaker(s): Sayan Mitra\, \n\nVirtual: https://events.vtools.ieee
 .org/m/441460
LOCATION:Virtual: https://events.vtools.ieee.org/m/441460
ORGANIZER:atreyee@ee.iitkgp.ac.in
SEQUENCE:10
SUMMARY:Safe control and estimation with coarse measurements
URL;VALUE=URI:https://events.vtools.ieee.org/m/441460
X-ALT-DESC:Description: <br /><div>\n<div>\n<p class="MsoNormal">Interpreti
 ng visual signals introduces both challenges and opportunities in the desi
 gn of control and autonomous systems. This talk will explore two key conce
 pts that address these challenges. In the first part\, I will introduce<em
 >perception contracts</em>&mdash\;an innovative approach to analyzing visu
 al control systems that rely on Deep Neural Networks for state estimation.
  A perception contract provides an over-approximation of a state estimator
  while guaranteeing closed-loop system invariants. These contracts can be 
 automatically synthesized using data and model-based analysis and have bee
 n successfully applied to systems such as automated landing controllers an
 d lane-keeping systems. The second part of the talk will focus on algorith
 ms for computing <em>indistinguishable sets</em>&mdash\;sets of states tha
 t cannot be distinguished based on available visual data. These sets help 
 define the theoretical limits of visual control\, revealing the boundaries
  of what can be achieved with coarse measurements in dynamic environments.
  Throughout the talk\, I will mention various examples\, highlight the too
 ls available\, and discuss open problems that invite further exploration i
 n this area.</p>\n</div>\n</div>\n<p>&nbsp\;</p>
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