All robotics problems are partially observable. In some problems, the partial observability is relatively superficial, enabling solution via planning and/or learning methods that assume full observability. The modern embodied intelligence research community spends most of its time looking under this fully-observable lamppost . But many problems of significant importance are significantly partially observable.
We know that obtaining optimal (or even reasonably good) solutions to general partially observable Markov decision problems (POMDPs) is intractable or even undecidable. Is that a reason to ignore or give up on them?
I'll argue that it's not. The fact that humans and other animals can learn and behave effectively in extremely partially observable domains argues that there is at least a subclass of general POMDPs that can be solved efficiently. I am interested in finding the structures and regularities in the real physical world that render many partially observable problems tractable. This talk will be a combination of tutorial and speculation, hoping to incite discussion, and no actual recent research results.
Zoom Link: https://mit.zoom.us/j/7652207066.