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POMDPs for Risk-Aware Autonomy

William Curran, Cameron Bowie, and William D. Smart.
In "Proceedings of the AAAI Fall Symposium on Shared Autonomy in Research and Practice", 2016.

Although we would like our robots to have completely autonomous behavior, this is often not possible. Some parts of a task might be hard to automate, perhaps due to hard-to-interpret sensor information, or a complex environment. In this case, using shared autonomy or teleoperation is preferable to an error-prone autonomous approach. However, the question of which parts of a task to allocate to the human, and which to the robot can often be tricky. In this paper, we introduce A 3 P , a risk-aware algorithm that discovers when to hand off subtasks to a human assistant. A3P models the task as a Partially Observably Markov Decision Process (POMDP) and explicitly represents failures as additional state-action pairs. Based on the model, the algorithm allows the user to allocate subtasks the robot or the human in such a way as to manage the worst-case performance time for the overall task.

Paper: [PDF]

@inproceedings{aaai-fs-16b,
  author = {Curran, William and Bowie, Cameron and Smart, William D.},
  title = {{POMDP}s for Risk-Aware Autonomy},
  booktitle = {Proceedings of the {AAAI} Fall Symposium on Shared Autonomy in Research and Practice},
  year = {2016}
}