Efficient Language-Guided Reinforcement Learning for Resource Constrained Autonomous Systems
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Author/Creator ORCID
Date
2022-09-23
Type of Work
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Citation of Original Publication
A. Shiri, M. Navardi, T. Manjunath, N. R.Waytowich and T. Mohsenin, "Efficient Language-Guided Reinforcement Learning for Resource Constrained Autonomous Systems," in IEEE Micro, 2022, doi: 10.1109/MM.2022.3199686.
Rights
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Public Domain Mark 1.0
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Abstract
In this paper, we propose an energy-efficient architecture which is designed to receive both
images and text inputs as a step towards designing reinforcement learning agents that can
understand human language and act in real-world environments. We evaluate our proposed
method on three different software environments and a low power drone named Crazyflie to
navigate towards specified goals and avoid obstacles successfully. To find the most efficient
language-guided RL model, we implemented the model with various configurations of image
input sizes and text instruction sizes on the Crazyflie drone GAP8 which consists of 8 RISC-V
cores. The task completion success rate and onboard power consumption, latency, and memory
usage of GAP8 are measured and compared with Jetson TX2 ARM CPU and Raspberry Pi 4. The
results show that by decreasing 20% of input image size we achieve up to 78% energy
improvement while achieving an 82% task completion success rate