FPGA Acceleration of ROS2-Based Reinforcement Learning Agents
Why this work is in the frame
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Bibliographic record
Abstract
Reinforcement learning agents have shown very good results in robot control and navigation tasks, allowing robots to learn how to interact with an environment appropriately in a model-free manner. However, real-world robot systems have strict latency, power, and cost constraints, thus requiring special hardware consideration for the demanding computations of neural networks. Furthermore, reinforcement learning networks should be able to interface efficiently with the various other robot components. To address these challenges, we propose a method for applying FPGA hardware accelerators to robotics reinforcement learning agents at the inference stage and seamlessly integrating the FPGA hardware module to the robot system by automatically wrapping it in a Robot Operating System 2 (ROS2) node. The proposed system is evaluated in three OpenAI gym control environments: Cartpole-v1, Acrobot-v1, and Pendulum-v0. In the evaluation, both quantized and non-quantized reinforcement learning neural networks are used, and the proposed FPGA system is observed to provide up to a 3.69x speed up and up to 52.7x better performance per watt when compared to an agent running on a ROS2 node on a modern CPU.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it