LaMP: 3Dシーンフローを潜在運動事前分布として用いる視覚-言語-行動ポリシーの学習
LaMP: Learning Vision-Language-Action Policy with 3D Scene Flow as Latent Motion Prior
3Dシーンフローを潜在的な運動事前分布として組み込んだ二重エキスパート型VLAフレームワークを提案し、LIBEROや実機実験で従来手法を上回る成功率とOOD頑健性を達成した。
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著者: Xinkai Wang, Chenyi Wang, Yifu Xu, Mingzhe Ye, Fucheng Zhang, Jialin Tian, Xinyu Zhan, Lifeng Zhu, Cewu Lu, Lixin Yang
分類: cs.CV, cs.RO
原文アブストラクト
We introduce \textbf{LaMP}, a dual-expert Vision-Language-Action framework that embeds dense 3D scene flow as a latent motion prior for robotic manipulation.Existing VLA models regress actions directly from 2D semantic visual features, forcing them to learn complex 3D physical interactions implicitly.This implicit learning strategy degrades under unfamiliar spatial dynamics.LaMP addresses this limitation by aligning a flow-matching \emph{Motion Expert} with a policy-predicting \emph{Action Expert} through gated cross-attention.Specifically, the Motion Expert generates a one-step partially denoised 3D scene flow, and its hidden states condition the Action Expert without full multi-step reconstruction.We evaluate LaMP on the LIBERO, LIBERO-Plus, and SimplerEnv-WidowX simulation benchmarks as well as real-world experiments.LaMP consistently outperforms evaluated VLA baselines across LIBERO, LIBERO-Plus, and SimplerEnv-WidowX benchmarks, achieving the highest reported average success rates under the same training budgets. On LIBERO-Plus OOD perturbations, LaMP shows improved robustness with an average 9.7\% gain over the strongest prior baseline.Our project page is available at https://summerwxk.github.io/lamp-project-page/.
