Human-Robot Teaming in Aircraft Maintenance. Credit: Lufthansa Technik AG

Seminar Series

The Wisconsin Robotics Seminar Series (WRSS) is a monthly event hosted by Prof. Bilge Mutlu from the department of computer science at UW-Madison as part of the NASA University Leadership Initiative funded project “Effective Human-Robot Teaming to Advance Aviation Manufacturing”. The presentations and discussions are aimed to highlight challenges and interesting research across a range of topics in robotics. Anyone is welcome to join!

 

WHEN

The seminar is hosted on the first Wednesday of every month at 12pm CT for one hour. The format is a 45 minute talk followed by a 15 minute discussion.

WHERE

The seminar is live-streamed on youtube. You can join our email subscription list to hear about upcoming talks and get the streaming link. Previous talks are available here.

INTERESTED IN GIVING A TALK?

We are interested in having a wide range of speakers. If you might be interested in giving a talk, please email Mike Hagenow (mhagenow[at]wisc.edu) and Emmanuel Senft (esenft[at]wisc.edu) with subject ‘[Seminar Series]’.

Upcoming Talks

Robot learning from few demonstrations by exploiting the structure and geometry of data

Abstract:

A wide range of applications can benefit from robots acquiring manipulation skills by interaction with humans. In this presentation, I will discuss the challenges that such learning process encompasses, including representations for manipulation skills that can exploit the structure and geometry of the acquired data in an efficient way, the development of optimal control strategies that can exploit variations in manipulation skills, and the development of intuitive interfaces to acquire meaningful demonstrations.

From a machine learning perspective, the core challenge is that robots can only rely on a small number of demonstrations. The good news is that we can exploit bidirectional human-robot interaction as a way to collect better data. We can also rely on various structures that remain the same within a wide range of robotic tasks. Such structures include geometrical aspects, by extending learning strategies that have been originally developed for standard Euclidean space to Riemannian manifolds. In robotics, these manifolds include orientation, manipulability ellipsoids, graphs and subspaces. Another type of structure that we study relates to the organization of data as multidimensional arrays (also called tensors). These data appear in various robotic tasks, either as the natural organization of sensorimotor data (tactile arrays, images, kinematic chains), or as the result of preprocessing steps (moving time windows, covariance features). Tensor factorization techniques (also called tensor methods or multilinear algebra) can be used to learn from only few tensor datapoints, by exploiting the multidimensional nature of the data.

Another key challenge in robot skill acquisition is to link the learning aspects to the control aspects. Optimal control provides a framework that allows us to take into account the possible variations of a task, the uncertainty of sensorimotor information, and the movement coordination patterns, by relying on well grounded control techniques such as linear quadratic tracking, differential dynamic programming, and their extensions to model predictive controllers. The formulation draws explicit links with learning techniques, as we can recast these techniques as Gauss-Newton optimization problems formulated at trajectory level (in both control space and state space), which facilitates the links to probabilistic approaches.

Bio:

Dr Sylvain Calinon is a Senior Researcher at the Idiap Research Institute (https://idiap.ch), heading the Robot Learning & Interaction group. He is also a Lecturer at the Ecole Polytechnique Federale de Lausanne (EPFL). From 2009 to 2014, he was a Team Leader at the Italian Institute of Technology. From 2007 to 2009, he was a Postdoc in the Learning Algorithms and Systems Laboratory, EPFL, where he obtained his PhD in 2007. His research interests cover robot learning, human-robot collaboration and optimal control.
Website: https://calinon.ch

  • June 2, 2021: Dr. Bradley Hayes

Past Talks (All recordings here)