把持仮説を幾何事前分布として用いる軽量な空間-言語-行動モデル
GraSLA: A Lightweight Spatial-Language-Action Model with Grasp Hypotheses as Geometric Priors
6自由度把持検出の多様な把持仮説を幾何事前分布として連続行動方策に統合し、空間・言語基盤モデルと双方向クロスアテンションで結ぶ軽量な言語条件付きマニピュレーション手法を提案。LIBEROで平均成功率99.30%を達成。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Jibao Yuan, Zixiang Zhao, Wensong Xu, Siqi Lu, Shaofei Chen
分類: cs.RO
原文アブストラクト
This paper focuses on improving the behavioral diversity and effectiveness of language-conditioned manipulation policies trained through imitation learning. Recently, continuous-action policies have become mainstream in robotic manipulation, but the lack of diverse demonstrations tends to restrict their behavioral diversity. For grasping, a fundamental subtask of manipulation, 6-DoF grasp detection provides diverse grasp hypotheses, but open-loop execution constrains responsiveness in dynamic environments. This motivates us to combine the diversity of grasp hypotheses with the adaptability of continuous-action policies. To this end, we propose GraSLA, a lightweight and effective spatial-language-action model with grasp hypotheses as geometric priors. In particular, we employ spatial and language foundation models to encode scene geometry and task semantics, and integrate them with grasp hypotheses through bidirectional cross-attention for continuous action prediction. On LIBERO, GraSLA achieves an average success rate of 99.30%.
