All decks
  • Few Remarks on Benchmarks

    Presented at the Workshop in Benchmarking Robotics

  • Bayesian Optimization for Robotics

    Designing and tuning controllers for real-world robots is a daunting task which typically requires significant expertise and lengthy experimentation. Bayesian optimization has shown to be a successful approach to automate these tasks with little human expertise required. In this talk, I will discuss the main challenges of robot learning, and how BO helps to overcome some of them. Using as showcase real-world applications where BO proved to be effective, I will also discuss how the challenges encountered in robotics applications can guide the development of new BO algorithms.

  • Learning Model-based Control for (Aerial) Manipulation

    In this talk, I will discuss what I consider two of the crucial challenges of manipulation: the use of tactile sensors, and model-based control.‌ Humans make extensive use of touch, but integrating the sense of touch in robot control has traditionally proved to be a difficult task. As an alternative, we propose the use of data-driven machine learning methods, to learn complex multi-modal models from raw sensor measurements. I will conclude by discussing useful lessons we learned along the way, and how some of these lessons could be valuable in the context of aerial manipulation.

  • Robots and the Sense of Touch

    ‌Humans make extensive use of touch. However, integrating the sense of touch in robot control has traditionally proved to be a difficult task. In this talk, I will discuss how machine learning can help to provide robots with the sense of touch, and the benefits of doing so.‌