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SUMMARY:A mathematical theory of co-design
DTSTART:20160526T141500
DTEND:20160526T151500
DTSTAMP:20260921T013900Z
UID:e145affc2ef8cebab8d606d400a4d23858cb9c2cc789b4484ba50d83
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr Andrea Censi\, Laboratory of Information and Decision Syste
 ms at MIT\nEverything comes together in the field of robotics. The design 
 of a robotic system involves the choice of actuators\, the choice of senso
 rs\, the choice of the energy source\, the choice of the computation subst
 rate\, the choice of the representations\, the choice of the algorithms (p
 erception\, planning\, and control). The irreducible complexity of robotic
 s comes from the fact that the feasibility of the design depends on recurs
 ive “co-design constraints” among all those heterogenous domains. Curr
 ently\, the design of robotic systems is an “art”\; as the complexity 
 of robotic systems increases\, we will need to transition from relying on 
 expert craftsmanship to systematic theory and tools for design.\nI will de
 scribe a mathematical “theory of co-design” that is able to capture th
 e irreducible complexity of robotics\, and might be useful for other domai
 ns involving systems of comparable complexity. The objects of this theory 
 are “design problems”\, described as feasibility relations between “
 functionality provided” and “resources used”. The basic property stu
 died is a monotonicity property\, which is: if the required functionality 
 is increased\, the required resources do not decrease. This property is in
 trinsic\, in the sense that is invariant to any order-preserving reparamet
 erization. I will show that this family of “Monotone Co-Design Problems
 ” (MCDPs) is closed to composition operations through operations that ar
 e the equivalent of series\, parallel\, and feedback interconnection.\nThe
  queries that can be answered based on these models are of the form “min
 imize resources\, subject to minimal functionality provided” or\, dually
 \, “maximize functionality\, subject to maximum resources usage”. I wi
 ll show that weak assumptions of monotonicity are sufficient to allow a sy
 stematic solution procedure that finds the set of all non-dominating solut
 ions based on the elementary theory of fixed points on partially ordered s
 ets (Kleene/Tarski). The complexity depends on the richness of the functio
 nality/resources spaces (measured by height and width of their posets)\, a
 s well as the structure of the co-design graph.\nI will also describe a fo
 rmal language for describing MCDPs as well as a prototype interpreter/solv
 er. Open-source software is available at http://mcdp.mit.edu/.\nBio: Andre
 a Censi is a research scientist and principal investigator with the Labora
 tory of Information and Decision Systems at MIT. He received a Ph.D. from 
 the California Institute of Technology in Control & Dynamical Systems. Cur
 rently\, he is also Chief Technology Officer of Duckietown Engineering co.
 \, a fictional MIT-affiliated startup developing a fleet of autonomous veh
 icles.
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STATUS:CONFIRMED
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