Carolina Higuera

I am a 2nd year Ph.D. student in the Paul G. Allen School at University of Washington, advised by Professor Byron Boots. I'm interested in robot manipulation and tactile sensing. I am very proud to be a Fulbright scholar, cohort 2021 from Colombia 🇨🇴.

I received my M.S. in Electronics Engineering from Universidad de los Andes, Bogota, Colombia. There, I worked on multiagent reinforcement learning for traffic light signal control.

Contact: chiguera [at] cs [dot] washington [dot] edu

Email  /  Google Scholar

My research focuses on developing models that allow robot manipulators capable of interpreting their world through vision-based tactile perception. In my work, I have focused on tracking extrinsic contacts between object and environment. My goal is to learn policies that can control those contacts, to make robots versatile in manipulation tasks. Specifically, my research topics include tactile sensing, neural fields, and reinforcement learning.

Neural Contact Fields are an implicit representation for tracking extrinsic contact on an object surface (between object and environment) with vision-based tactile sensing (between robot hand and object).

Application of Deep RL for an Energy Management System (EMS) and its comparison with respect to classical techniques such as Rule-Based and Model Predictive Control.

Application of MARL to traffic light signal control to reduce travel time. We simulated a network with six signalized intersections in SUMO, using real data from the Transit Department of Bogota, Colombia. This project was my Master thesis!

A system to analyze urban traffic using computer vision to get realiable information of traffic flow in Bogota, Colombia.

Neural Contact Fields are an implicit representation for tracking extrinsic contact on an object surface (between object and environment) with vision-based tactile sensing (between robot hand and object).

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