AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Scaling generalist robotic policies is limited by the high cost of collecting embodied interaction data. Large-scale egocentric human videos offer a rich alternative, but they lack robot action labels, and existing action annotation methods are often noisy under real-world visual complexity and diverse manipulation dyn...
Generalist robot policies have demonstrated strong generalization across robotic manipulation tasks, yet their success rates remain limited in com- plex long-horizon scenarios. Recent methods improve Visual-Language-Action (VLA) policies through online reinforcement learning on real robots, but such training relies on...
WLA$^3$ (World Latent Action Modeling for Semantics, Dynamics, and Kinematics), a unified generalist policy model framework built around representations learned by a World Latent Action Model (WLAM), is introduced.
Results demonstrate that physical understanding pre-training genuinely benefits downstream manipulation inVision-language-action models, and introduce GAP tokens, a compact learnable bottleneck that isolates task-agnostic motion regularities and lets the action head consume physical commonsense without corrupting the p...
This work introduces Event3R, a feed-forward framework that directly maps asynchronous event streams to globally consistent 3D point clouds, and proposes a Masked Bin Modeling strategy for self-supervised pre-training, enabling robust temporal representation learning with minimal labeled data.
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