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 ic-engineering-colloquium-series-alex-lagrassa
Robotic Engineering Colloquium Series - Alex LaGrassa
Combining Learned and Structured Knowledge for Robot Planning\n\n\n\n      \n      \n\n\n\nAbstract:
  Machine learning is transforming robots' ability to perform tasks in dive
 rse, real-world scenarios, but state-of-the-art learning methods often req
 uire extensive data and computational resources, limiting their accessibil
 ity to many communities. My work focuses on planning algorithms that combi
 ne the adaptability of machine learning with the efficiency and reliabilit
 y of classical robotics methods. In this talk, we will build conceptual mo
 dels for combining physics-based reasoning in classical robotics with tool
 s from machine learning to enable planning in complex scenarios, such as p
 lant watering. We will also explore strategies to solve real-world tasks u
 sing planning despite the inaccurate or incomplete predictive models avail
 able to us. I will discuss how these ideas inform how we can prepare each 
 other for adaptive problem-solving in diverse contextsBio: Alex LaGrassa (
 they/them) is a PhD candidate in the Robotics Institute at Carnegie Mellon
  University researching methods that combine machine learning with classic
 al robotics by quantifying then expanding robot capabilities. They develop
  algorithms that equip robots with the intelligence to manipulate challeng
 ing but common deformable objects such as plants, cables, and liquid with 
 limited access to data and computational resources. Alex is also passionat
 e about inclusive STEM education so all communities contribute to shaping 
 technological development. They enjoy designing hands-on robotics teaching
  opportunities that invite students to participate fully in the learning p
 rocess and contribute their unique perspectives.\n
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