Auto-TransRL: ロボット知覚のための視覚パイプラインの自律的構成
Auto-TransRL: Autonomous Composition of Vision Pipelines for Robotic Perception
Transformerと深層強化学習を組み合わせ、視覚タスクに適したアルゴリズムを自動推薦して視覚パイプラインを構築する手法を提案。未知のアルゴリズムにも汎化する。
著者: Aditya Kapoor, Nijil George, Vartika Sengar, Vighnesh Vatsal, Jayavardhana Gubbi
分類: cs.CV, cs.RO
原文アブストラクト
Creating a vision pipeline for different datasets to solve a computer vision task is a complex and time consuming process. Currently, these pipelines are developed with the help of domain experts. Moreover, there is no systematic structure to construct a vision pipeline apart from relying on experience, trial and error or using template-based approaches. As the search space for choosing suitable algorithms for achieving a particular vision task is large, human exploration for finding a good solution requires time and effort. To address the following issues, we propose a dynamic and data-driven way to identify an appropriate set of algorithms that would be fit for building the vision pipeline in order to achieve the goal task. We introduce a Transformer Architecture complemented with Deep Reinforcement Learning to recommend algorithms that can be incorporated at different stages of the vision workflow. This system is both robust and adaptive to dynamic changes in the environment. Experimental results further show that our method also generalizes well to recommend algorithms that have not been used while training and hence alleviates the need of retraining the system on a new set of algorithms introduced during test time.
