Corpus ID: 220302150

Designing Environments Conducive to Interpretable Robot Behavior

@article{Kulkarni2020DesigningEC,
  title={Designing Environments Conducive to Interpretable Robot Behavior},
  author={Anagha Kulkarni and S. Sreedharan and Sarah Keren and T. Chakraborti and David E. Smith and S. Kambhampati},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.00820}
}
  • Anagha Kulkarni, S. Sreedharan, +3 authors S. Kambhampati
  • Published 2020
  • Computer Science
  • ArXiv
  • Designing robots capable of generating interpretable behavior is essential for effective human-robot collaboration. This requires robots to be able to generate behavior that aligns with human expectations but exhibiting such behavior in arbitrary environments could be quite expensive for robots, and in some cases, the robot may not even be able to exhibit expected behavior. However, in structured environments (like warehouses, restaurants, etc.), it may be possible to design the environment so… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 24 REFERENCES
    Explicable Robot Planning as Minimizing Distance from Expected Behavior
    26
    Plan explicability and predictability for robot task planning
    80
    Legibility and predictability of robot motion
    340
    Generating Plans that Predict Themselves
    16
    Balancing Explicability and Explanation in Human-Aware Planning
    25
    A Concise Introduction to Models and Methods for Automated Planning
    193
    Goal Recognition Design with Stochastic Agent Action Outcomes
    21
    Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy
    126
    Goal Recognition Design
    67