因果的にバイアス除去された潜在行動モデルによる身体的行動条件付き世界モデル
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models
未ラベル動画から潜在行動を推定する既存モデルは、背景など行動と無関係な要素を混入させる問題を抱える。本論文では、因果的バイアス除去フレームワークCD-LAMを提案し、身体中心の再構成・行動中心の対比学習・潜在空間較正により、制御可能で堅牢な潜在行動表現を実現した。
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著者: Yufan Wei, Kun Zhou, Lingjun Mao, Zijun Zhang, Ziming Xu, Ziqiao Xi, Shuang Liang, Ruobing Han, Yuchen Yan, Xinyue Wang, Fan Feng, Biwei Huang
分類: cs.CV, cs.RO
原文アブストラクト
Action-conditioned world models (ACWMs) aim to simulate future observations conditioned on embodied actions, offering a promising foundation for robot planning, policy evaluation, and data augmentation. However, learning controllable ACWMs requires large-scale action-labeled data, which remains costly to collect in the real world. Latent action models (LAMs) mitigate this bottleneck by inferring latent actions from unlabeled videos, but existing LAMs are typically trained with reconstruction-only objectives and therefore entangle action-relevant dynamics with action-irrelevant visual factors such as backgrounds and untouched objects. In this work, we identify this action-irrelevant bias as a key obstacle to controllable ACWMs and introduce evaluation metrics to measure latent-action bias, action following, and robustness. We propose CD-LAM, a causally debiased framework for LAM-based ACWMs. CD-LAM introduces three efficient fine-tuning objectives: embodiment-centric reconstruction, action-centric contrastive learning, and latent space calibration, which together encourage embodiment-focused, action-aware, and calibrated non-collapsed latent action representations. Experiments on 2B and 14B ACWM backbones show that CD-LAM substantially improves latent-action controllability, downstream robot-action following, visual fidelity, and adaptation efficiency, requiring only 6k fine-tuning steps and more than 12$\times$ fewer robot-action adaptation updates than the baseline.
