SA-Net: Deep Neural Network for Robot Trajectory Recognition from RGB-D Streams
| Autor: | Soans, Nihal, Asali, Ehsan, Hong, Yi, Doshi, Prashant |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | ICRA 2020, pp. 2153-2159 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/ICRA40945.2020.9197393 |
| Popis: | Learning from demonstration (LfD) and imitation learning offer new paradigms for transferring task behavior to robots. A class of methods that enable such online learning require the robot to observe the task being performed and decompose the sensed streaming data into sequences of state-action pairs, which are then input to the methods. Thus, recognizing the state-action pairs correctly and quickly in sensed data is a crucial prerequisite for these methods. We present SA-Net a deep neural network architecture that recognizes state-action pairs from RGB-D data streams. SA-Net performed well in two diverse robotic applications of LfD -- one involving mobile ground robots and another involving a robotic manipulator -- which demonstrates that the architecture generalizes well to differing contexts. Comprehensive evaluations including deployment on a physical robot show that \sanet{} significantly improves on the accuracy of the previous method that utilizes traditional image processing and segmentation. Comment: (in press) |
| Databáze: | arXiv |
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