Maria Bauza Villalonga, MIT
Location: 122 Gates Hall
Abstract: Reliable robots must understand their environment and act on it with precision. Practical robots should also be able to achieve wide generalization; i.e, a single robot should be capable of solving multiple tasks. For instance, we would like to have, but still lack, a robot that can reliably assemble most IKEA furniture instead of having one robot tailored to each piece of furniture. Towards this, in this talk, I will present an approach to robotic pick-and-place that provides robots with both high-precision and generalization skills. The proposed approach uses only simulation to learn probabilistic models of grasping, planning, and localization that transfer with high accuracy to the actual robotic system. In real experiments, we show that our dual-arm robot is capable to exert task-aware picks on new objects, use visuo-tactile sensing to localize them, and perform dexterous placings of these objects that involve in-hand regrasps and tight placing requirements with less than 1mm of tolerance. Overall, our proposed approach can handle new objects and placing configurations, providing the robot with precise generalization skills.
Bio: Maria Bauza Villalonga is a PhD student in Robotics at the Massachusetts Institute of Technology, working with Professor Alberto Rodriguez. Before that, she received Bachelor’s degrees in Mathematics and Physics from CFIS, an excellence center at the Polytechnic University of Catalonia. Her research focuses on achieving precise robotic generalization by learning probabilistic models of the world that allow robots to reuse their skills across multiple tasks with high success.
Maria has received several fellowships including Facebook, NVIDIA, or LaCaixa fellowships. Her research has obtained awards such as Best Paper Finalist in Service Robotics at ICRA 2021, Best Cognitive Paper award at IROS 2018, and Best Paper award finalist at IROS 2016. She was also part of the MIT-Princeton Team participating in the Amazon Robotics Challenge, winning the stowing task in 2017 and receiving the 2018 Amazon Best Systems Paper Award in Manipulation.