LCSR Seminar: Michael Posa

Description

Michael Posa, an assistant professor in mechanical engineering and applied mechanics at the University of Pennsylvania, will give a talk titled "Implicit Learning and Control for Data-efficient, Dexterous Manipulation" for the Laboratory for Computational Sensing + Robotics. Posa leads the Dynamic Autonomy and Intelligent Robotics lab and focuses on developing algorithms to enable robots to operate both dynamically and safely as they interact with their environments.

Abstract:

As we ask our robotic systems to become more capable, with the ultimate aim of deploying robots into complex and ever-changing scenarios, the vast space of potential tasks drives the need for flexibility and generalization. For all the promise of big-data machine learning, what will happen when robots deploy to our homes and workplaces and inevitably encounter new objects, new tasks, and new environments? With the goal of rapid adaptation to novel settings, I'll discuss our progress on real-time multi-contact MPC for dexterous manipulation, where we can realize dynamic motions which dynamically and intelligently make and break contact, with only a simple goal as the given objective. Control, however, requires a model, and so I will present our recent results on contact-inspired implicit model learning, where, by embedding convex optimization, we reshape the loss landscape and enable more accurate training, better generalization, and ultimately data efficiency. Lastly, given time, I'll discuss how model learning and control can synergize in interesting ways: via online adaptation or by synthesizing task-relevant models which identify key aspects of multi-contact dynamics necessary to achieve a goal.