The robotics field has been deeply influenced by the advent of deep learning. In recent years, this trend has been characterized by the adoption of large, pretrained models for robotic use cases, which are not compatible with the computational hardware available in robotic systems. Moreover, such large, computationally intensive models impede the low-latency execution which is required for many closed-loop control systems. In this work, we propose different strategies for improving the computational efficiency of the deep-learning models adopted in reinforcement-learning (RL) scenarios. As a use-case project, we consider an image-based RL method on the synergy between push-and-grasp actions. As a first optimization step, we reduce the model architecture in complexity, by decreasing the number of layers and by altering the architecture structure. Second, we consider downscaling the input resolution to reduce the computational load. Finally, we perform weight quantization, where we compare post-training quantization and quantized-aware training. We benchmark the improvements introduced in each optimization by running a standard testing routine. We show that the optimization strategies introduced can improve the computational efficiency by around 300 times, while also slightly improving the functional performance of the system. In addition, we demonstrate closed-loop control behaviour on a real-world robot, while processing everything on a Jetson Xavier NX edge device.
Keywords: neural networks; optimization; post-training quantization; quantized-aware training; reinforcement learning.