FRAM: 軌道ガイドによる視覚特徴選択で実現するコンパクトな言語条件付きロボットマニピュレーション
FRAM: Trajectory-Guided Visual Feature Selection for Compact Language-Conditioned Robot Manipulation
将来のエンドエフェクタ軌道を画像上のポインタとして使い、必要な局所視覚特徴だけを読む小型のVLAポリシーを提案。LIBEROで92.2%の成功率を達成し、実機UR5eでもカップ積みを実現した。
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著者: Hiroshi Ito, Hyogo Hiruma, Yoshiki Kanai, Takahiro Yoshida, Akira Kanazawa, Hiroki Yamada
分類: cs.RO
原文アブストラクト
Vision-Language-Action models achieve strong performance in robot manipulation, but often require large numbers of parameters. In this work, we propose the Future Representation Action Model (FRAM), a small policy that explicitly links the future end-effector trajectory to the current visual input. FRAM uses the image coordinates of the predicted trajectory as spatial pointers and reads local visual features related to the motion from the current image. This organizes the information for action generation into the reference position (Where), the visual state (What), and the future motion (Future). Trajectory labels are generated automatically from demonstrations and camera geometry, so no manual annotation is needed. With 138.7M parameters, including a frozen language encoder, FRAM reaches an average success rate of 92.2% over the four standard LIBERO suites, close to the 94.2% of $π_0$ with 3.3B parameters. Without extra training, it also reaches an average of 67.3% on LIBERO-Plus. Ablations confirm that both the future trajectory and the local visual features improve performance and robustness. On a real dual-arm UR5e, FRAM stacks cups using only wrist cameras, including choosing and switching between the left and right arms. These results show that selecting visual information based on future motion is an effective way to obtain both high performance and robustness in a small robot policy.