Abstract
We present a framework that, given a set of skills a robot can perform, abstracts sensor data into symbols that are used to automatically encode the robot’s capabilities in Linear Temporal Logic (LTL). We specify reactive high-level tasks based on these capabilities, for which a strategy is automatically synthesized and executed on the robot, if the task is feasible. If a task is not feasible given the robot’s capabilities, our framework automatically suggests additional skills for the robot that would make the task feasible. We demonstrate our framework on a Baxter robot manipulating blocks on a table.
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Notes
- 1.
Note that \(\sigma \) are only generated from effect sets in Konidaris et al. [1], future work will consider generating \(\sigma \) from precondition sets as well.
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This work is supported by the ONR PERISCOPE MURI award N00014-17-1-2699.
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Pacheck, A., Konidaris, G., Kress-Gazit, H. (2022). Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations. In: Asfour, T., Yoshida, E., Park, J., Christensen, H., Khatib, O. (eds) Robotics Research. ISRR 2019. Springer Proceedings in Advanced Robotics, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-030-95459-8_31
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