Augment-Connect-Explore：データ不足下での視覚行動計画のためのパラダイム
Augment-Connect-Explore: a Paradigm for Visual Action Planning with Data Scarcity
データが少ない状況でも生画像から直接行動計画を立てられるよう、訓練データの拡張・潜在空間グラフの接続生成・新領域の探索を組み合わせたACEパラダイムを提案し、箱積みと布折りのタスクで有効性を示した。
著者: Martina Lippi, Michael C. Welle, Petra Poklukar, Alessandro Marino, Danica Kragic
分類: cs.RO
原文アブストラクト
Visual action planning particularly excels in applications where the state of the system cannot be computed explicitly, such as manipulation of deformable objects, as it enables planning directly from raw images. Even though the field has been significantly accelerated by deep learning techniques, a crucial requirement for their success is the availability of a large amount of data. In this work, we propose the Augment-Connect-Explore (ACE) paradigm to enable visual action planning in cases of data scarcity. We build upon the Latent Space Roadmap (LSR) framework which performs planning with a graph built in a low dimensional latent space. In particular, ACE is used to i) Augment the available training dataset by autonomously creating new pairs of datapoints, ii) create new unobserved Connections among representations of states in the latent graph, and iii) Explore new regions of the latent space in a targeted manner. We validate the proposed approach on both simulated box stacking and real-world folding task showing the applicability for rigid and deformable object manipulation tasks, respectively.
