My research focuses on developing algorithms for precise robotic generalization:
making robots capable of solving many tasks without compromising their performance and reliability.
By learning general probabilistic models of perception and control,
we can provide robots with the right tools to thrive in diverse environments and task requirements.
In my work, I have studied how learning probabilistic models allows precise control,
and how developing accurate visuo-tactile perception enables solving complex tasks,
such as grasping, localization, and precise placing without prior experience.
My goal is to continue developing algorithms that make robots dexterous and versatile at manipulating their environment.
Our latest work: precise pick-and-placing of objects without prior experience! Why is this important? Currently, industry can't solve
this problem for a large variety of objects. Our system can, enabling robotics solutions in a wide variety of applications where flexibility is key.
December 2021 Invited talk at the Washington University robotics colloquium.
November 2021 Invited talks at Stanford and the CMU Manipulation discussion grup.
October 2021 Invited talk at Cornell Robotic Seminar and selected to attend the Rising Stars in EECS.
July 2021 Attended the 2021 RSS Pioneers Workshop.
May 2021 Best Paper Finalist Award on Service Robotics at ICRA 2021
March 2021 Invited talk at University of Toronto, AI in Robotics Seminar.
October 2020 Invited talk at University of Pennsylvania, Grasp Seminar.
May 2020 Co-organizing workshop at ICRA 2020 on Uncertainty in Contact-Rich Interactions.
November 2019 Selected to attend the Global Young Scientists Summit. Awarded to only 5 PhDs from all MIT departments.
October 2019 Rising Stars in Mechanical Engineering. Awarded to 30 graduate and postdoctoral women.
Januray 2019 Awarded the Facebook Emerging Scholar Award. 21 awardees out of more than 900 applications.
December 2018 Awarded the NVIDIA Graduate Fellowship. Given to 10 PhD students from more than 230 applications.
Towards precise generalization of robot skills: accurate pick-and-place of novel objects" M. Bauza, T. Bronars, Y. Hou, N. Chavan-Dafle, A. Rodriguez
in progress , 2022
We learn in simulation how to accurate pick-and-place objects with visuo-tactile perception. Our solution transfers to the real world and succefully handles diferent types of objects shapes without requiring prior experience.
FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedback
J. Zhao, M. Bauza, E. Adelson
under review, 2022
We address the problem of using visuo-tactile feedback for 6-DoF localization and 3D reconstruction of unknown in-hand objects.