VolumeDP: 操作ポリシー学習のためのボリューム表現モデリング
VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning
2D画像から直接3D動作を予測する既存手法の2D-3Dミスマッチを解消するため、画像特徴を3Dボリューム表現に変換し、タスク関連ボクセルを選択して動作予測を行う新しいポリシーアーキテクチャを提案した。
詳しい要約
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著者: Tianxing Zhou, Feiyang Xue, Zhangchen Ye, Tianyuan Yuan, Hang Zhao, Tao Jiang
分類: cs.RO
原文アブストラクト
Imitation learning is a prominent paradigm for robotic manipulation. However, existing visual imitation methods map 2D image observations directly to 3D action outputs, imposing a 2D-3D mismatch that hinders spatial reasoning and degrades robustness. We present VolumeDP, a policy architecture that restores spatial alignment by explicitly reasoning in 3D. VolumeDP first lifts image features into a Volumetric Representation via cross-attention. It then selects task-relevant voxels with a learnable module and converts them into a compact set of spatial tokens, markedly reducing computation while preserving action-critical geometry. Finally, a multi-token decoder conditions on the entire token set to predict actions, thereby avoiding lossy aggregation that collapses multiple spatial tokens into a single descriptor. VolumeDP achieves a state-of-the-art average success rate of 88.8% on the LIBERO simulation benchmark, outperforming the strongest baseline by a substantial 14.8% improvement. It also delivers large performance gains over prior methods on the ManiSkill and LIBERO-Plus benchmarks. Real-world experiments further demonstrate higher success rates and robust generalization to novel spatial layouts, camera viewpoints, and environment backgrounds. Code and videos are available on the project page: https://yzc0731.github.io/VolumeDP/
