VLAff: 統一的な操作可能アフォーダンスのための視覚言語アフォーダンスモデル
VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances
人間のビデオから操作スキルを学習するため、物体中心のアフォーダンスを抽出し、視覚・把握・軌道のアフォーダンスを統合的に生成する大規模視覚言語モデルを提案した。
詳しい要約
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著者: Jihoon Oh, Kento Kawaharazuka, Kei Okada
分類: cs.RO, cs.CV
原文アブストラクト
Learning manipulation skills from human videos is promising for scalable robot learning. However, the embodiment mismatch between humans and robots makes this challenging. One promising solution is to learn object-centric actionable affordances that are embodiment-agnostic. In this work, we propose a framework that leverages egocentric human videos with state-of-the-art 3D Structure-from-Motion and hand mesh reconstruction to extract actionable affordances such as visual, grasp, and trajectory affordances that explicitly encode where to interact, how to grasp, and how to move. We construct EgoAffordance, a large-scale dataset comprising 204K episodes with 5.6M visual affordances and 11.6M grasp and trajectory affordances. Building on this, we introduce VLAff, a large vision-language model-based unified foundation model that learns cross-modal correlations across all actionable affordances. Given a visual observation and instruction, VLAff generates visual affordance heatmaps, grasp poses, and trajectories, which are then converted into directly executable actions by utilizing 3D scene information. Through extensive experiments, we demonstrate that VLAff not only achieves state-of-the-art performance on visual affordance prediction, but can also be effectively applied to real robot applications such as zero-shot manipulation and affordance-guided robot learning.