• Stanford 2022

    [Stanford - 30 Sep 2022] Touch is a crucial sensor modality in both humans and robots. Recent advances in tactile sensing hardware have resulted -- for the first time -- in the availability of mass-produced, high-resolution, inexpensive, and reliable tactile sensors. In this talk, I will argue for the importance of creating a new computational field of "Touch processing" dedicated to the processing and understanding of touch, similarly to what computer vision is for vision. This new field will present significant challenges both in terms of research and engineering. To start addressing some of these challenges, I will introduce our open-source ecosystem dedicated to touch sensing research. Finally, I will present some applications of touch in robotics and discuss other future applications.

  • MIT

  • [Samsung]

  • Beyond One-step Ahead [UC Berkeley]

  • 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.

  • [ViTac - ICRA]

  • Theory and Practice of Model-based Reinforcement Learning [Caltech]

  • Perceiving, Understanding, and Interacting through Touch [UC Berkeley]

    [UC Berkeley - 29 Apr 2021]

  • Data-efficient Optimization with Bayesian Optimization

    [M2L - 13 January 2021]

  • Towards a Science of Touch Processing

    [RSS Workshop on Visuo-tactile Sensors for Robust Manipulation: From Perception to Control]

  • Towards In-hand Manipulation From Touch

    [ICRA WS ViTaC - 31 May 2020]

  • Introduction to Bayesian Optimization

    [cs188 - UC Berkeley - 10 April 2020]

  • Rethinking Model-based Reinforcement Learning

    [Berkeley]

  • 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

    [RSS 2019 Workshop on Aerial Interaction and Manipulation - 23 June 2019] 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.‌