Stefanos Nikolaidis - Towards Robust HRI: A Stochastic Optimization Approach
From Katie Gentilello on April 9th, 2021
In this talk, I propose formulating the problem of automatic scenario generation in HRI as a quality diversity problem, where the goal is not to find a single global optimum, but a diverse range of failure scenarios that explore both environments and human actions. I show how standard quality diversity algorithms can discover interesting and diverse scenarios in the shared autonomy domain. I then propose a new quality diversity algorithm, CMA-ME, that achieves significantly better performance than the state-of-the-art in benchmark domains. Finally, I discuss applications in procedural content generation and human preference learning.