The Priberam Machine Learning Lunch Seminars are a series of informal meetings which occur every two weeks at Instituto Superior Técnico, in Lisbon. It works as a discussion forum involving different research groups, from IST and elsewhere. Its participants are interested in areas such as (but not limited to): statistical machine learning, signal processing, pattern recognition, computer vision, natural language processing, computational biology, neural networks, control systems, reinforcement learning, or anything related (even if vaguely) with machine learning.

The seminars last for about one hour (including time for discussion and questions) and revolve around the general topic of Machine Learning. The speaker is a volunteer who decides the topic of her presentation. Past seminars have included presentations about state-of-the-art research, surveys and tutorials, practicing a conference talk, presenting a challenging problem and asking for help, and illustrating an interesting application of Machine Learning such as a prototype or finished product.

Presenters can have any background: undergrads, graduate students, academic researchers, company staff, etc. Anyone is welcome both to attend the seminar as well as to present it. Ocasionally we will have invited speakers. See below for a list of all seminars, including the speakers, titles and abstracts.

Note: The seminars are held at lunch-time, and include delicious free food.

Feel free to join our mailing list, where seminar topics are announced beforehand. You may also visit the mailing list webpage. Anyone can attend the seminars; no registration is necessary. If you would like to present something, please send us an email.

The seminars are usually held every other Tuesday, from 1 PM to 2 PM, at the IST campus in Alameda. This sometimes changes due to availability of the speakers, so check regularly!

Tuesday, May 31st 2016, 13h00 - 14h00

Zita Marinho (ISR)

Robotic Motion Planning in Reproducing Kernel Hilbert Spaces

Anfiteatro do Complexo Interdisciplinar

Instituto Superior Técnico - Alameda


In this seminar I will present my work on trajectory optimization for robot motion planning in Reproducing Kernel Hilbert Spaces (RKHSs).
Functional gradient algorithms are a popular choice for motion planning in complex many-degree-of-freedom robots. They work by directly optimizing a continuous trajectory that avoids obstacles while maintaining geometric properties such as smoothness. We exploit this fact and propose a functional gradient based method under RKHSs.
This generalization lets us represent trajectories as linear combinations of kernel functions. Depending on the selection of kernel, we can directly optimize in spaces of trajectories that are inherently smooth in velocity, jerk, curvature, etc., and that have a low-dimensional, adaptively chosen parameterization. I will present some experiments that illustrate the effectiveness of the planner for different kernels, including Gaussian RBFs with independent and coupled interactions among robot joints, Laplacian RBFs, and B-splines.


Bio: Zita is a PhD student in the CMU/Portugal program jointly advised by Andre Martins at Unbabel/IT IST, Geoffrey Gordon at ML/CMU and Siddhartha Srinivasa at Robotics Institute/CMU. Her interests focus on machine learning methods using semi-supervision and her PhD thesis is focused on spectral methods for learning in Natural Language and Robotics. She holds a Masters in Robotics (CMU) and in Physics Engineering from IST, Portugal.