  Robots operating in real-world human environments will likely encounter task
execution failures. To address this, we would like to allow co-present humans
to refine the robot's task model as errors are encountered. Existing approaches
to task model modification require reasoning over the entire dataset and model,
limiting the rate of corrective updates. We introduce the State-Indexed Task
Updates (SITU) algorithm to efficiently incorporate corrective demonstrations
into an existing task model by iteratively making local updates that only
require reasoning over a small subset of the model. In future work, we will
evaluate this approach with a user study.
