The development of robots that can learn to interact with the world and manipulate the objects in it has emerged as one of the greatest and so far largely unsolved challenges in robotics research. In this talk, I will argue that the development of such advanced machines requires a transition from classical manual design with purely model-based control to a novel synthesis paradigm. We need to allow the machine to autonomously develop its own blueprint and algorithmically generate its topological, kinematic, and dynamic self. Building on this, it shall develop controls for its own body as it moves, learns to manipulate objects in a controlled way, and sensitively interacts with the world.

Drawing from our work in torque-controlled lightweight robots towards human-safe tactile robots that can manipulate, fly, or drive, I outline the technological quantum leaps that recently have taken place. In particular, this progress was made possible by a human-centered design, soft and force-sensitive control, contact reflexes, and model-based machine learning. In the real world, by enabling human-robot coexistence, collaboration, and interaction for the first time, this robotic technology has proven transformative to traditional manufacturing already around the globe. Increasingly, it is now impacting professional services, domestic applications, medicine and healthcare.

After that, I will use our current work to chart the path toward the next generation of tactile machines. We have taken first steps towards autonomously designing and building machines that have the ability to learn their self and thus adapt to changes in body topology and ultimately their entire dynamics. Finally, I will present recent results on designing modular control and learning architectures that achieve complex behaviors for challenging manipulation problems while being provably stable.

Robots with a sense of touch: self-replicating the machine and learning the self

**Presenter:** [Sami Haddadin](https://underline.io/speakers/216806-sami) (Technical University of Munich) 
**Talk Title:** Robots with a sense of touch: self-replicating the machine and learning the self 
 **Abstract:** The development of robots that can learn to interact with the world and manipulate the objects in it has emerged as one of the greatest and so far largely unsolved challenges in robotics research. In this talk, I will argue that the development of such advanced machines requires a transition from classical manual design with purely model-based control to a novel synthesis paradigm. We need to allow the machine to autonomously develop its own blueprint and algorithmically generate its topological, kinematic, and dynamic self. Building on this, it shall develop controls for its own body as it moves, learns to manipulate objects in a controlled way, and sensitively interacts with the world.

Drawing from our work in torque-controlled lightweight robots towards human-safe tactile robots that can manipulate, fly, or drive, I outline the technological quantum leaps that recently have taken place. In particular, this progress was made possible by a human-centered design, soft and force-sensitive control, contact reflexes, and model-based machine learning. In the real world, by enabling human-robot coexistence, collaboration, and interaction for the first time, this robotic technology has proven transformative to traditional manufacturing already around the globe. Increasingly, it is now impacting professional services, domestic applications, medicine and healthcare.

After that, I will use our current work to chart the path toward the next generation of tactile machines. We have taken first steps towards autonomously designing and building machines that have the ability to learn their self and thus adapt to changes in body topology and ultimately their entire dynamics. Finally, I will present recent results on designing modular control and learning architectures that achieve complex behaviors for challenging manipulation problems while being provably stable.

AAAI / IAAI-23 Invited talk Sami Haddadin

keynote

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Sami Haddadin

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Robots with a sense of touch: self-replicating the machine and learning the self

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