What do you really want to do? Towards a Theory of Intentions for Human-Robot Collaboration

Rocio Gomez, Mohan Sridharan, Heather Riley

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
123 Downloads (Pure)


The architecture described in this paper encodes a theory of intentions based on the key principles of non-procrastination, persistence, and automatically limiting reasoning to relevant knowledge and observations. The architecture reasons with transition diagrams of any given domain at two different resolutions, with the fine-resolution description defined as a refinement of, and hence tightly-coupled to, a coarse-resolution description. For any given goal, nonmonotonic logical reasoning with the coarse-resolution description computes an activity, i.e., a plan, comprising a sequence of abstract actions to be executed to achieve the goal. Each abstract action is implemented as a sequence of concrete actions by automatically zooming to and reasoning with the part of the fine-resolution transition diagram relevant to the current coarse-resolution transition and the goal. Each concrete action in this sequence is executed using probabilistic models of the uncertainty in sensing and actuation, and the corresponding fine-resolution outcomes are used to infer coarse-resolution observations that are added to the coarse-resolution history. The architecture’s capabilities are evaluated in the context of a simulated robot assisting humans in an office domain, on a physical robot (Baxter) manipulating tabletop objects, and on a wheeled robot (Turtlebot) moving objects to particular places or people. The experimental results indicate improvements in reliability and computational efficiency compared with an architecture that does not include the theory of intentions, and an architecture that does not include zooming for fine-resolution reasoning.
Original languageEnglish
Pages (from-to)179-208
Number of pages30
JournalAnnals of Mathematics and Artificial Intelligence
Issue number1-2
Early online date24 Mar 2020
Publication statusPublished - Feb 2021

Bibliographical note

Funding Information:
The authors thank Michael Gelfond for discussions related to the modeling of defaults and exogenous actions in the architecture reported in this paper. This work was supported in part by the Asian Office of Aerospace Research and Development award FA2386-16-1-4071 and the U.S. Office of Naval Research Science of Autonomy Award N00014-17-1-2434. Opinions and conclusions in this article are those of the authors.

Publisher Copyright:
© 2020, The Author(s).


  • Human-robot collaboration
  • Knowledge representation and reasoning
  • Non-monotonic logical reasoning
  • Probabilistic reasoning
  • Theory of intentions

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics


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