Marc Toussaint

I am professor for Intelligent Systems at TU Berlin since 2020, spend
some time at MIT in 2017/18, some time with Amazon Robotics in 2017,
and was professor for Machine Learning and Robotics at U Stuttgart
since 2012. My research interests are in the intersection of AI and
robotics, in particular combining Machine Learning, optimization, and
AI reasoning to tackle fundamental research questions in
robotics. Integrating learning and reasoning is of particular interest
to me. Concrete research topics we work on are models and algorithms
for physical reasoning, task-and-motion planning (logic-geometric
programming), learning representations, control, and learning to
transfer model-based strategies to reactive and adaptive real-world
behavior. Some of my earlier work was on the planning-as-inference
paradigm, relational reinforcement learning, active learning, and way
back also on evolutionary algorithms, and black holes (my diploma was
on gravity theory).

Talk Title: Planning in the Age of Learning

Abstract:

Task and Motion Planning (TAMP) became a standard problem formulation
within robotics to describe the complex behavior we strive for. However, despite the word Planning in TAMP, it doesn’t mean that one has to use planning methods in the traditional sense — it rather characterizes the behavior we want to see. In this talk I will discuss own work on TAMP, covering both, planning (i.e., optimization & sampling) and learning methods, and try to clarify these characteristics we want to see. I will also spend some time discussing whether “getting things to work” is our only goal, and the “call for more data” the solution.