Seeing Space and Motion: Enhancing Latent Actions with Spatial and Dynamic Awareness for VLA

• URL: http://arxiv.org/abs/2509.26251v1
• Date: Tue, 30 Sep 2025 13:41:43 GMT
• Title: Seeing Space and Motion: Enhancing Latent Actions with Spatial and Dynamic Awareness for VLA
• Authors: Zhejia Cai, Yandan Yang, Xinyuan Chang, Shiyi Liang, Ronghan Chen, Feng Xiong, Mu Xu, Ruqi Huang,
• Abstract summary: Latent Action Models (LAMs) enable Vision- Language-Action systems to learn semantic action rep- resentations from large-scale unannotated data.<n>We propose Farsighted-LAM, a latent action framework with geometry- aware spatial encoding and multi-scale temporal modeling.<n>We further propose SSM-VLA, an end- to-end VLA framework built upon Farsighted-LAM, which integrates structured perception with a visual Chain-of-Thought module.
• Score: 21.362682837521632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Latent Action Models (LAMs) enable Vision- Language-Action (VLA) systems to learn semantic action rep- resentations from large-scale unannotated data. Yet, we identify two bottlenecks of LAMs: 1) the commonly adopted end-to-end trained image encoder suffers from poor spatial understanding; 2) LAMs can be fragile when input frames are distant, leading to limited temporal perception. Such factors inevitably hinder stable and clear action modeling. To this end, we propose Farsighted-LAM, a latent action framework with geometry- aware spatial encoding and multi-scale temporal modeling, capturing structural priors and dynamic motion patterns from consecutive frames. We further propose SSM-VLA, an end- to-end VLA framework built upon Farsighted-LAM, which integrates structured perception with a visual Chain-of-Thought module to explicitly reason about environmental dynamics, enhancing decision consistency and interpretability. We validate SSM-VLA on multiple VLA tasks in both simulation and real- world settings, and achieve state-of-the-art performance. Our results demonstrate that our strategy of combining geometry- aware modeling, temporal coherence, and explicit reasoning is effective in enhancing the robustness and generalizability of embodied intelligence.

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