AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Vision-Language-Action (VLA) foundation models have scaled rapidly to enhance manipulation performance and generalizability, but this scaling incurs high computational costs that render real-world deployment increasingly challenging. Existing approaches typically mitigate this issue by designing smaller architectures o...
Vision-Language-Action (VLA) models increasingly rely on action experts that generate short action chunks under receding-horizon control. While chunk-level training is convenient across robot embodiments, it optimizes local action likelihood without explicitly accounting for long-horizon task success. Sequence-level re...
Adjoint Guidance Flow is proposed, which is a deterministic optimal control problem, whose optimal guidance is a costate that carries the terminal critic gradient back through the remaining flow, and regress the guidance network onto this costate while keeping both the VLA and critic frozen.
This work proposes Action Upcycling, a training-free algorithm that reuses actions the policy would otherwise discard, without accessing model internals or drawing extra samples, and finds that discarded actions stay close to their replanned versions as long as the action velocity remains smooth.
DPP enables real-time dynamic manipulation on a single consumer GPU without additional training on dynamic data and constructs a counterfactual observation that places a predicted target position in a familiar robot context, allowing the model to invoke an existing manipulation skill rather than generate a recovery beh...
Curved Ray Expectation Positional Encoding (CRePE) is introduced, which represents each image token as a depth-aware distribution along its Unified Camera Model (UCM) ray and integrates the expected rotary positional phasor along the curved path this distribution traces when projected into each query view.
QWERTY is introduced, a training-free framework that enables flexible motion control in pretrained image-to-video DiTs via user-defined object warping and optical flow and achieves the most effective motion control among existing training-free approaches on a recent image-to-video DiT, with performance comparable to fi...
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