AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 5 of 14 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
ModAR is introduced, the first WAM to autoregressively denoise multiple future modalities before predicting actions, and it is found that ModAR's sequential generation outperforms existing WAM formulations, with the highest average success rate at all evaluated data scales.
It is shown that a flexible and expressive transformer, PointZero, outperforms prior methods on the same data and demonstrates the utility of the pre-training objective by post-training PointZero for two downstream applications: action-conditioned 3D dynamics prediction and imitation learning.
Fine-tuning a Vision-Language-Action (VLA) model for a new deployment environment is expensive, yet most methods apply uniform-capacity adapters to every network region as if every region requires equal adjustment. This paper tests that assumption on five architecturally diverse VLAs (OpenVLA-OFT, $\pi_0$, SmolVLA, DTP...
This work introduces Wiggle and Go!, a two-stage framework for zero-shot rope manipulation: a brief, safe wiggle action is observed to predict descriptive rope parameters, which then conditions a trajectory optimizer for zero-shot goal-conditioned execution.
While Vision-Language-Action (VLA) models pretrained on large-scale robot datasets provide a strong foundation for robot manipulation, their performance can degrade when adapted to new tasks with limited task-specific demonstrations. Retrieval offers a practical way to reuse existing demonstrations for data-efficient a...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.