目標指向ロボット視覚のための早期融合
Early Fusion for Goal Directed Robotic Vision
目標情報を視覚処理の初期段階で融合させ、タスク特化の表現を生成するEarlyFusionモデルを提案し、模倣学習によるロボット物品取得タスクで有効性を示した。
著者: Aaron Walsman, Yonatan Bisk, Saadia Gabriel, Dipendra Misra, Yoav Artzi, Yejin Choi, Dieter Fox
分類: cs.CV, cs.RO
原文アブストラクト
Building perceptual systems for robotics which perform well under tight computational budgets requires novel architectures which rethink the traditional computer vision pipeline. Modern vision architectures require the agent to build a summary representation of the entire scene, even if most of the input is irrelevant to the agent's current goal. In this work, we flip this paradigm, by introducing EarlyFusion vision models that condition on a goal to build custom representations for downstream tasks. We show that these goal specific representations can be learned more quickly, are substantially more parameter efficient, and more robust than existing attention mechanisms in our domain. We demonstrate the effectiveness of these methods on a simulated robotic item retrieval problem that is trained in a fully end-to-end manner via imitation learning.
