見て行動し、プロンプトで指定する：視覚言語行動ポリシーのベイズ分解
Seeing to Act, Prompting to Specify: A Bayesian Factorization of Vision Language Action Policy
VLAモデルのファインチューニングで生じる言語忘却と視覚ショートカットを、ポリシーを視覚行動事前と言語条件付き尤度にベイズ分解することで解決し、未知の指示・物体・環境への汎化性能を向上させた。
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著者: Kechun Xu, Zhenjie Zhu, Anzhe Chen, Shuqi Zhao, Qing Huang, Yifei Yang, Haojian Lu, Rong Xiong, Masayoshi Tomizuka, Yue Wang
分類: cs.RO, cs.CV
原文アブストラクト
The pursuit of out-of-distribution generalization in Vision-Language-Action (VLA) models is often hindered by catastrophic forgetting of the Vision-Language Model (VLM) backbone during fine-tuning. While co-training with external reasoning data helps, it requires experienced tuning and data-related overhead. Beyond such external dependencies, we identify an intrinsic cause within VLA datasets: modality imbalance, where language diversity is much lower than visual and action diversity. This imbalance biases the model toward visual shortcuts and language forgetting. To address this, we introduce BayesVLA, a Bayesian factorization that decomposes the policy into a visual-action prior, supporting seeing-to-act, and a language-conditioned likelihood, enabling prompt-to-specify. This inherently preserves generalization and promotes instruction following. We further incorporate pre- and post-contact phases to better leverage pre-trained foundation models. Information-theoretic analysis formally validates our effectiveness in mitigating shortcut learning. Extensive experiments show superior generalization to unseen instructions, objects, and environments compared to existing methods. Project page is available at: https://xukechun.github.io/papers/BayesVLA.
