Computational Optimization of Image-Based Reinforcement Learning for Robotics.

ABSTRACT: 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 num

SUBMITTER: Ferraro S 

PROVIDER: S-EPMC9571553 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature