Towards Robust HRI: A Stochastic Optimization Approach
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The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automatically evaluate novel algorithms and applications. Exploring the diverse scenarios of interaction between humans and robots in simulation can improve understanding of complex HRI systems and avoid potentially costly failures in real-world settings. 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.
- IRIM Seminar Series