Learning to interact with the world: when generality meets precision
Robotics stands as one of the most impactful and promising endeavors of our times. Learning to interact with the world is fundamental for solving some of our most pressing societal challenges: from taking care of our aging population and aiding with labor-intensive jobs to assisting in climate-related disasters and rescue emergencies. In this talk, I will argue that such a level of autonomy and performance requires robots that can excel across diverse settings while remaining accurate and reliable.
My talk will focus on how we can develop learning algorithms that foster robotics generalization while ensuring the desired task performance. First, I will present a learning approach to pose estimation for novel objects based on visuo-tactile sensing that doesn’t rely on real data and results in accurate pose distributions. Then, I will demonstrate how this approach enables precise robotic pick-and-place using task-aware grasping. The robotic system reasons over the models for grasping, planning, and perception in order to optimize its actions based only on simulated data. In real experiments, we demonstrate that our approach learned purely in simulation, allows robots to successfully manipulate new objects and perform highly accurate placements.
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 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 and environments.
Maria has received several fellowships, including Facebook, NVIDIA, and 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 ITOS 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.
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