ContextVLA: Vision-Language-Action Model with Amortized Multi-Frame Context

• URL: http://arxiv.org/abs/2510.04246v1
• Date: Sun, 05 Oct 2025 15:29:57 GMT
• Title: ContextVLA: Vision-Language-Action Model with Amortized Multi-Frame Context
• Authors: Huiwon Jang, Sihyun Yu, Heeseung Kwon, Hojin Jeon, Younggyo Seo, Jinwoo Shin,
• Abstract summary: Leveraging temporal context is crucial for success in partially observable robotic tasks.<n>Prior work in behavior cloning has demonstrated inconsistent performance gains when using multi-frame observations.<n>We introduce ContextVLA, a policy model that robustly improves robotic task performance by effectively leveraging multi-frame observations.
• Score: 54.58057019521198
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Leveraging temporal context is crucial for success in partially observable robotic tasks. However, prior work in behavior cloning has demonstrated inconsistent performance gains when using multi-frame observations. In this paper, we introduce ContextVLA, a policy model that robustly improves robotic task performance by effectively leveraging multi-frame observations. Our approach is motivated by the key observation that Vision-Language-Action models (VLA), i.e., policy models built upon a Vision-Language Model (VLM), more effectively utilize multi-frame observations for action generation. This suggests that VLMs' inherent temporal understanding capability enables them to extract more meaningful context from multi-frame observations. However, the high dimensionality of video inputs introduces significant computational overhead, making VLA training and inference inefficient. To address this, ContextVLA compresses past observations into a single context token, allowing the policy to efficiently leverage temporal context for action generation. Our experiments show that ContextVLA consistently improves over single-frame VLAs and achieves the benefits of full multi-frame training but with reduced training and inference times.

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