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DTSTAMP:20230619T231844Z
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DTSTART;TZID=America/New_York:20230615T180000
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DESCRIPTION:From the development of foundational state space estimation too
 ls like the Kalman filter to state of the art machine learning techniques 
 for sensor fusion and decision making\, probabilistic models and reasoning
  algorithms are the “lingua franca” for modern robotics and autonomous
  systems. The COHRINT Lab at CU develops and leverages probabilistic AI in
  new and unique ways to tackle fundamental research questions for current 
 and futuristic systems. Dr. Nisar Ahmed will highlight his lab’s recent 
 work on human-machine/robot interaction for collaborative information gath
 ering and reasoning\, using probabilistic Bayesian state estimation and de
 cision-making algorithms. These methods not only plug in seamlessly to exi
 sting autonomy architectures\, but also exploit the ability of human colla
 borators to provide semantic data (via user-friendly interfaces) that is r
 ich with useful “out of band” information for autonomous platforms. In
  essence\, these methods open the door to “soft re-programming” of aut
 onomous reasoning from the outside by end-users (who are not robotics expe
 rts or computer scientists). Aerospace applications such as integrated UAS
  surveillance/reconnaissance\, UAS-enabled wilderness search and rescue\, 
 and remote robotic space exploration will be demonstrated and discussed.\n
 \nSpeaker(s): Dr. Nisar Ahmed\n\nRoom: 426\, Bldg: Beatty Hall\, 550 Hunti
 ngton Ave\, Boston\, Massachusetts\, United States\, 02115
LOCATION:Room: 426\, Bldg: Beatty Hall\, 550 Huntington Ave\, Boston\, Mass
 achusetts\, United States\, 02115
ORGANIZER:giovanni-miraglia@outlook.com
SEQUENCE:16
SUMMARY:Building Better Bots with Bayes: Probabilistic Human-Machine Reason
 ing for Collaborative Autonomy
URL;VALUE=URI:https://events.vtools.ieee.org/m/360942
X-ALT-DESC:Description: <br /><p>From the development of foundational state
  space estimation tools like the Kalman filter to state of the art machine
  learning techniques for sensor fusion and decision making\, probabilistic
  models and reasoning algorithms are the &ldquo\;lingua franca&rdquo\; for
  modern robotics and autonomous systems. The COHRINT Lab at CU develops an
 d leverages probabilistic AI in new and unique ways to tackle fundamental 
 research questions for current and futuristic systems. Dr. Nisar Ahmed wil
 l highlight his lab&rsquo\;s recent work on human-machine/robot interactio
 n for collaborative information gathering and reasoning\, using probabilis
 tic Bayesian state estimation and decision-making algorithms. These method
 s not only plug in seamlessly to existing autonomy architectures\, but als
 o exploit the ability of human collaborators to provide semantic data (via
  user-friendly interfaces) that is rich with useful &ldquo\;out of band&rd
 quo\; information for autonomous platforms. In essence\, these methods ope
 n the door to &ldquo\;soft re-programming&rdquo\; of autonomous reasoning 
 from the outside by end-users (who are not robotics experts or computer sc
 ientists). Aerospace applications such as integrated UAS surveillance/reco
 nnaissance\, UAS-enabled wilderness search and rescue\, and remote robotic
  space exploration will be demonstrated and discussed.&nbsp\; &nbsp\; <str
 ong>&nbsp\;</strong></p>
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