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X-APPLE-TRAVEL-ADVISORY-BEHAVIOR:AUTOMATIC
234251
20260406T130041Z
DTSTART;TZID=America/New_York:20260430T140000
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 0260430T150000
URL;TYPE=URI:https://www.wpi.edu/news/calendar/events/robot
 ics-engineering-masters-thesis-presentation-samuel-honor
Robotics Engineering Master\'s Thesis Presentation: Samuel Honor
Complex-Valued GNNs for Distributed Basis-Invariant Control of Planar Systems\n\n\n\n      \n      \n\n\n\nAbstract:
  Graph neural networks (GNNs) are a well-regarded tool for learned control
  of networked dynamical systems due to their ability to be deployed in a d
 istributed manner. However, current distributed GNN architectures assume t
 hat all nodes in the network collect geometric observations in compatible 
 bases, which limits the usefulness of such controllers in GPS-denied and c
 ompass-denied environments. This paper presents a GNN parametrization that
  is globally invariant to choice of local basis. 2D geometric features and
  transformations between bases are expressed in the complex domain. Inside
  each GNN layer, complex-valued linear layers with phase-equivariant activ
 ation functions are used. When viewed from a fixed global frame, all polic
 ies learned by this architecture are strictly invariant to choice of local
  frames. This architecture is shown to increase the data efficiency, track
 ing performance, and generalization of learned control when compared to a 
 real-valued baseline on an imitation learning flocking task.\nAdvisor: Pro
 fessor Kevin LeahyCommittee: Professor Carlo Pinciroli, Professor Guanrui 
 Li\n
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