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Process Overview

This package uses visual feature learning to create detectors for operating the behaviors included (drawer opening, flip style light switches, and rocker style light switches). Before using the included behaviors on real world mechanisms, you'll first need to perform an initialization and a data collection step to create a visual classifier for your specific mechanism. After these two steps, you'll be able to run the included behaviors with your new learned visual detector using the execute launch script (execute.launch).

Specifically, the steps that you'll need to perform are:

roslaunch trf_learn trf_learn.launch

roslaunch trf_learn init.launch

roslaunch trf_learn practice.launch

Finally to execute your learned behavior use:

roslaunch trf_learn execute.launch

When initialization finishes, you'll progress to the second stage and set the robot to gather additional data autonomously for its classifier (using practice.launch). Due to the experimental nature of the code, currently this data collection process takes a few hours but once it's done use the task execution script (execute.launch) to activate the behavior with its trained detector.

2024-07-20 14:46