Sufficient Symbols to Make Optimization-Based Manipulation Planning Tractable

ABSTRACT: Not only is combined Task and Motion Planning a hard problem, it also relies on an appropriate symbolic representation to describe the task level, which to find is perhaps an even more fundamental problem. I first briefly report on our work that considered symbol learning and, relatedly, manipulation skill learning on its own. However, I now believe that what are appropriate abstractions should depend on their use in higher level planning. I will introduce Logic-Geometric Programming as a framework in which the role of the symbolic level is to make optimization over complex manipulation paths tractable. Similarly to enumerating categorical aspects of an otherwise infeasible problem, such as enumerating homotopy classes in path planning, or local optima in general optimization. I then report on recent results we got with this framework for combined task and motion planning and human-robot cooperative manipulation planning.

BIO:
Marc Toussaint currently visiting scholar at CSAIL until summer 2018. He is full professor for Machine Learning and Robotics at the University of Stuttgart since 2012. Before he was assistant professor and leading an Emmy Noether research group at FU & TU Berlin. His research focuses on the combination of decision theory and machine learning, motivated by fundamental research questions in robotics. Specific interests include combining geometry, logic and probabilities in learning and reasoning, and appropriate representations and priors for real-world manipulation learning.