視覚行動計画のロバスト性向上のためのアンサンブル潜在空間ロードマップ
Ensemble Latent Space Roadmap for Improved Robustness in Visual Action Planning
複数の潜在空間ロードマップの計画をアンサンブルし、類似度に基づいて最適な計画を選択することで、視覚に基づく行動計画のロバスト性を高める手法を提案した。
著者: Martina Lippi, Michael C. Welle, Andrea Gasparri, Danica Kragic
分類: cs.RO
原文アブストラクト
Planning in learned latent spaces helps to decrease the dimensionality of raw observations. In this work, we propose to leverage the ensemble paradigm to enhance the robustness of latent planning systems. We rely on our Latent Space Roadmap (LSR) framework, which builds a graph in a learned structured latent space to perform planning. Given multiple LSR framework instances, that differ either on their latent spaces or on the parameters for constructing the graph, we use the action information as well as the embedded nodes of the produced plans to define similarity measures. These are then utilized to select the most promising plans. We validate the performance of our Ensemble LSR (ENS-LSR) on simulated box stacking and grape harvesting tasks as well as on a real-world robotic T-shirt folding experiment.
