Bayesian active learning for collaborative task specification using equivalence regions

Nils Wilde, Dana Kulic, Stephen L J Smith

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)


Specifying complex task behaviors while ensuring good robot performance may be difficult for untrained users. We study a framework for users to specify rules for acceptable behavior in a shared environment such as industrial facilities. As non-expert users might have little intuition about how their specification impacts the robot's performance, we design a learning system that interacts with users to find an optimal solution. Using active preference learning, we iteratively show alternative paths that the robot could take on an interface. From the user feedback on ranking the alternatives, we learn about the weights that users place on each part of their specification. We extend the user model from our previous work to a discrete Bayesian learning model and introduce a greedy algorithm for proposing alternative that operates on the notion of equivalence regions of user weights. We prove that using this algorithm, the revision active learning process converges on the user-optimal path. Using simulations performed in realistic industrial environments, we demonstrate the convergence and robustness of our approach.

Original languageEnglish
Article number8633961
Pages (from-to)1691-1698
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
Publication statusPublished - 1 Apr 2019


  • Cognitive human-robot interaction
  • motion and path planning

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