Abstract

The next generation of robotic manipulation systems will witness an increase in skills thanks to novel AI paradigms, enabling them to handle complex tasks in unstructured environments and adapt to unexpected circumstances. For autonomous robotic systems to be relevant in practical real-world applications, the learning process must be both data efficient and safe, leveraging on a priori knowledge and models about the environment and tasks. This includes the vital ability to transfer skills between applications and robotic systems while ensuring constant safety under all conditions.  

By facilitating discussion among invited speakers, participants, and authors of contributed papers, the workshop aims to study and discuss the following fundamental open research questions:

• How can we take advantage of robotic priors, scene structure, and demonstrations to accelerate robotic learning?

• How can control theory be integrated into the learning framework to enforce system theoretic properties? 

• How can a robot efficiently acquire the skills needed for purposeful and high-performance manipulation?

• How can we ensure safety when the robot agent needs to physically explore an unknown environment?

• How can we minimize the reliance on real-world data in the learning process?

Topic of Interest

Speakers

Workshop General Chairs