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Nadia Figueroa

PhD Student


ME A3 424


  • Office: +41 21 69 31060


  • (2016) Coordinated Multi-Arm Motion Planning: Reaching for Moving Objects in the Face of Uncertainty: Coordinated control strategies for multi-robot systems are necessary for tasks that cannot be executed by a single robot. This encompasses tasks where the workspace of the robot is too small or where the load is too heavy for one robot to handle. Using multiple robots makes the task feasible by extending the workspace and/or increase the payload of the overall robotic system. In this paper, we consider two instances of such task: a co-worker scenario in which a human hands over a large object to a robot; intercepting a large flying object. The problem is made difficult as the pick-up/intercept motions must take place while the object is in motion and because the object's motion is not deterministic. The challenge is then to adapt the motion of the robotic arms in coordination with one another and with the object. Determining the pick-up/intercept point is done by taking into account the workspace of the multi-arm system and is continuously recomputed to adapt to change in the object's trajectory. We propose a dynamical systems (DS) based control law to generate autonomous and synchronized motions for a multi-arm robot system in the task of reaching for a moving object. We show theoretically that the resulting DS coordinates the motion of the robots with each other and with the object, while the system remains stable. We validate our approach on a dual-arm robotic system and demonstrate that it can re-synchronize and adapt the motion of each arm in synchrony in a fraction of seconds, even when the motion of the object is fast and not accurately predictable.

  • (2016) Learning Complex Sequential Tasks from Demonstration: A Pizza Dough Rolling Case We introduce a hierarchical framework that is capable of learning complex sequential tasks from human demonstrations through kinesthetic teaching, with minimal human intervention. Via an automatic task segmentation and action primitive discovery algorithm, we are able to learn both the high-level task decomposition (into action primitives), as well as low-level motion parameterizations for each action, in a fully integrated framework. In order to reach the desired task goal, we encode a task metric based on the evolution of the manipulated object during demonstration, and use it to sequence and parametrize each action primitive. We illustrate this framework with a pizza dough rolling task and show how the learned hierarchical knowledge is directly used for autonomous robot execution.


    Conference Proceedings

  • 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. Accepted. [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]

    Nadia Figueroa is a Ph.D. student in the Learning Algorithms and Systems Laboratory (LASA) at the Swiss Federal Institute of Technology in Lausanne (EPFL) since October 2013. She received her M.Sc. in Automation and Robotics from TU Dortmund (Germany) in 2012 and B.Sc. in Mechatronics from Monterrey Tech (Mexico) in 2007. During her master studies, she developed her thesis in the Institute of Robotics and Mechatronics (RM) at the German Aerospace Center (DLR). It addressed the problem of self-verification of the complex kinematic chains of the humanoid robot Rollin' Justin by using on-board and external sensory systems.

    She then served as a Research Assistant at the Engineering Division of New York University (NYU) Abu Dhabi, where she worked on novel algorithms for contextual scene labeling, indoor mapping and 3D modeling using Kinect-like sensors, as well as sEMG data analysis and muscle force control of bionic arms.

    Her current research focuses on developing and applying machine learning techniques for automatic segmentation, task decomposition and primitive motion modeling of complex sequential tasks in the Robot Learning from Demonstration (LfD) paradigm.

    Research Abstract

    For a detailed publication list, check her profiles on ResearchGate, Academia.edu or Google Scholar.

    Prior work: For an overview of Nadia's research prior to LASA, check this seminar she gave at NYU AD in 2013 regarding 3D computer vision and it's applications to robotics and multimedia. You can also view the following short video of her master thesis project at DLR (2012) concerning the verification of a humanoid's upper body kinematics using an on-board stereo camera system.

Last update: 11/03/08