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Member figueroa_fernan

Nadia Figueroa

PhD Student

Email:
nadia.figueroafernandez@epfl.ch

Room:
ME A3 424

Phones:

  • Office: +41 21 69 31060

Personal Home Page:
lasa.epfl.ch/files/NadiaCV2018.pdf

Fields of Interest

  • Learning from Demonstration
  • Incremental/Interactive Learning
  • Human-Robot Collaboration
  • Multi-Robot Coordination
  • Shared Autonomy and Control


Projects

  • The main goal of my research is to endow robots with the capabilities of learning, aiding and smoothly interacting with humans in unstructured and complicated tasks, from household activities such as cooking and cleaning to difficult industrial tasks such as assemblies, carrying/lifting large objects, etc.
  • In order to achieve this goal, I focus on leveraging machine learning techniques with concepts from dynamical systems theory to solve salient problems in the areas of learning from demonstration, incremental/interactive learning, human-robot collaboration, multi-robot coordination, shared autonomy and control.
  • I've worked on the following research projects which have been funded by the EU projects ROBOHOW and COGIMON.

    (2015/2016) Learning Complex Sequential Tasks from Demonstration: Uni-Manual and Bi-Manual Case Studies

    (2016/2017) Development of Multi-Arm Coordination Strategies

    (2016/2017) Learning Complex Joint-Space Behaviors for Single and Multi-Arm Systems

  • For a detailed publication list, check my Google Scholar profile.
  • I am also passionate about teaching machine learning concepts and algorithms. Throughout my PhD I've been the head teaching assistant for graduate-level courses: Advanced Machine Learning and Machine Learning Programming for which I co-developed ML_toolbox : A Machine learning toolbox in MATLAB, containing algorithms for non-linear dimensionality reduction, clustering, classification and regression along with examples and tutorials. I am the main architect and developer of the syllabus for Machine Learning Programming .
  • I am a firm believer in code benchmarking and experiment replication. For this reason I make all of my code available online either on my personal github account or in LASA's github account
  • Main Organizer of the Tutorial on Dynamical System (DS)-based Learning from Demonstration (LfD). A 3 hour long tutorial that introduces students to techniques used to (1) program robots through human demonstration by using DS (2) modulate DS for obstacle avoidance or surface following and (3) DS-based impedance/force control strategies. Website found here .


Hobbies

  • Traveling, eating and drinking wine.


Media Coverage

  • (2017) Top 5 Finalist for the KUKA Innovation Award 2017



Publications



    Journals

  • Mirrazavi Salehian, S. S., Figueroa, N. and Billard, A. (2018) A Unified Framework for Coordinated Multi-Arm Motion Planning. The International Journal of Robotics Research. [home page] [show abstract] [BibTeX]
    Source code from this publication available here
  • Yonadav Shavit, Figueroa, N., Mirrazavi Salehian, S. S. and Billard, A. (2018) Learning Augmented Joint-Space Task-Oriented Dynamical Systems: A Linear Parameter Varying and Synergetic Control Approach. IEEE Robotics and Automation Letters (RA-L). [home page] [show abstract] [BibTeX]
    Source code from this publication available here


  • Conference Proceedings

  • Figueroa, N. and Billard, A. (2018) A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning. In Proceedings of the 2nd Conference on Robot Learning (CoRL) - 2018 . Accepted. [CORL 2018]
  • Mirrazavi Salehian, S. S., Figueroa, N. and Billard, A. (2017) Dynamical System-based Motion Planning for Multi-Arm Systems: Reaching for moving objects. In Proceedings of International Joint Conference on Artificial Intelligence 2017, Melbourne, Australia. [IJCAI 2017] [show abstract] [pdf] [BibTeX]
    Source code from this publication available here
  • Mirrazavi Salehian, S. S., Figueroa, N. and Billard, A. (2016) Coordinated multi-arm motion planning: Reaching for moving objects in the face of uncertainty. In Proceedings of Robotics: Science and Systems XVI , Arbor, Michigan, USA. Received Best Student paper Award. Also nominated as the Best Conference Paper Award. Best Student Paper Award. Best Systems Paper Award.. [RSS 2016] [home page] [show abstract] [BibTeX]
    Source code from this publication available here
  • Beetz, M., Bessler, D., Winkler, J., Bartels, G., Billard, A., Figueroa, N., Pais, A. L. and et al. (2016) Open Robotics Research Using Web-based Knowledge Services. In Proceedings of the International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 2016. [BibTeX]
  • Figueroa, N., Pais, A. L. and Billard, A. (2016) Learning Complex Sequential Tasks from Demonstration: A Pizza Dough Rolling Case Study. In Proceedings of the 2016 ACM/IEEE International Conference on Human-Robot Interaction. HRI Pioneers Workshop. [HRI 2016] [show abstract] [pdf] [BibTeX]
    Source code from this publication available here


  • Other Publications

  • Figueroa, N. and Billard, A. (2018) Modeling Compositions of Impedance-based Primitives via Dynamical Systems. In Proceedings of the Workshop on Cognitive Whole-Body Control for Compliant Robot Manipulation (COWB-COMP). IEEE International Conference on Robotics and Automation. [ICRA 2018] [home page] [show abstract] [pdf]
  • Figueroa, N., Mirrazavi Salehian, S. S. and Billard, A. (2018) Multi-Arm Self-Collision Avoidance: A Sparse Solution for a Big Data Problem. In Proceedings of the Third Machine Learning in Planning and Control of Robot Motion (MLPC) Workshop. IEEE International Conference on Robotics and Automation. [ICRA 2018] [home page] [show abstract] [pdf] [BibTeX]
  • Figueroa, N. and Billard, A. (2017) Invariant and Weakly-Parameterized Algorithms for Efficient Robot Learning: Tackling the Micro-Data Challenge. Presented at the Workshop on Micro-data: the next frontier in robot learning?. [IROS'2017] [home page]
  • Figueroa, N. and Billard, A. (2017) Learning Complex Manipulation Tasks from Heterogeneous and Unstructured Demonstrations. In Proceedings of Workshop on Synergies between Learning and Interaction. IEEE/RSJ International Conference on Intelligent Robots and Systems. [IROS'2017] [home page] [show abstract] [pdf] [BibTeX]



Last update: 11/03/08