Organizers: Ali-Akbar Agha-Mohammadi, Hanna Kurniawati, Christopher Amato
Autonomous robots must be able make good decisions in the presence of sensor and outcome uncertainty. The Partially Observable Markov Decision Process (POMDP) is the general and principled framework to address such decision problems. POMDP-based methods have been used widely in robotics, with a number of successes and failures. The main objective of this workshop is to bring researchers together to discuss the recent developments in POMDPs and the remaining open problems that limit their applicability. We will explore if POMDPs can become an "everyday tool" in robotics. We will discuss the reasons why this hasn't happened yet and what we can do to overcome these roadblocks. We will also discuss existing applications of POMDPs in robotics, learn the "tips and tricks" in applying POMDPs to physical robots, and discuss the gap between theory and practice. We hope to bring together researchers working on POMDP-based methods from theory to applications in order to share knowledge and explore ways to incorporate new methods and identify interesting new problems to tackle.