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cs.CV2026
TurboWAM: Learning What Matters for Fast World Action Models
Guile Wu, Shuangzhi Li, Xingxin Chen +5
World action models have demonstrated promising performance in robot policy learning, yet their high inference latency remains a critical bottleneck for real-world deployment. Rece…
cs.CV2026
Anticipate Before Acting: Future-State-Conditioned Vision-Language Navigation
Lingfeng Zhang, Zhanguang Zhang, Liheng Ma +2
End-to-end vision-language navigation (VLN) with causal vision-language models maps instructions and egocentric observations directly to actions, but standard behavior cloning supe…
cs.CV2026
Uncertainty-Aware Gaussian Map for Vision-Language Navigation
Jianzhe Gao, Rui Liu, Yuxuan Xu +6
Vision-Language Navigation (VLN) requires an agent to navigate 3D environments following natural language instructions. During navigation, existing agents commonly encounter percep…