# Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations

arXiv: https://arxiv.org/abs/2204.08327 · HTML: https://mlsanity.com/paper/2204.08327/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2022.

## Built on by (most cited first, 1 of 1)

- [Efficient Recovery Learning using Model Predictive Meta-Reasoning](https://mlsanity.com/paper/2209.13605/) (2022)

## Abstract (arXiv)

We present a framework that, given a set of skills a robot can perform, abstracts sensor data into symbols that we use to automatically encode the robot's capabilities in Linear Temporal Logic. 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, we present two methods, one enumeration-based and one synthesis-based, for automatically suggesting additional skills for the robot or modifications to existing skills that would make the task feasible. We demonstrate our framework on a Baxter robot manipulating blocks on a table, a Baxter robot manipulating plates on a table, and a Kinova arm manipulating vials, with multiple sensor modalities, including raw images.