arXiv Machine Learning July 15, 2026 By Yuanjie Lu, Beichen Wang, Zhengqi Wu, Yang Li, Xiaomin Lin, Chengzhi Mao, Xuesu Xiao

APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model

Read the original on arXiv Machine Learning →

arXiv:2603. 08862v2 Announce Type: replace-cross Abstract: Autonomous navigation in highly constrained environments remains challenging for mobile robots.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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Vision-Language-Policy Model for Dynamic Robot Task Planning

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RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning

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LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies

arXiv:2606. 15768v1 Announce Type: cross Abstract: Vision-Language-Action models (VLAs) leverage large-scale vision-language pretraining for semantic robot control, but often lack explicit foresight into how robot actions change the scene.

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LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving

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arXiv AI

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Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models

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Can Vision Foundation Models Navigate? Zero-Shot Real-World Evaluation and Lessons Learned

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