AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,156 papers
An operational taxonomy, a coverage comparison against the eight closest surveys, an evaluation loop that isolates the advantage of prediction, and an actionable protocol of four advantage-aware metrics anchored on named testbeds are contributed.
MISCO is developed, a novel evolutionary framework empowered by deep generative models to optimize VSR designs with theoretical guarantees that represents a step change towards more scalable and reliable soft robot development.
This work presents RoboLDA, a Bayesian probabilistic model that decomposes VSR morphology generation into a four-level hierarchy:"task-robot-organ-voxel", and is trained via variational inference, which pioneers hierarchical generative modeling of robot morphology.
The results suggest that the frontier of Physical AI depends not only on stronger action models, but also on executable harnesses that integrate perception, task understanding and reasoning, and action execution into a unified, verifiable, and feedback-driven system.
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DAWN is a noise-robust perception framework for legged locomotion which builds noise robustness directly into a world model via two modifications: feeding noisy depth to the encoder while keeping clean depth as the reconstruction target, forcing the model to implicitly denoise its input.
This work proposes an LLM chaining architecture that separates instruction classification and action generation into two specialized stages, reducing per-inference prompt length by approximately 45% while improving planning consistency.
This work proposes embodiment-conditioned safety filtering, in which a Hamilton-Jacobi reachability-based value function and its corresponding safety-maximizing policy are shared across robots, and performs Hamilton-Jacobi reachability analysis directly in latent space so that the learned safety concepts can generalize...
This work introduces a unified formalism for proactive robot assistance, organize it into three levels, and provides a framework to address the highest level of unprompted proactive assistance, and presents a method, GAP, that instantiates the framework, learning from passive observation to anticipate user goals and ac...
Generalization in robotic manipulation requires policies to perform tasks across diverse unseen object instances that vary in shape, size, and pose. However, conventional behavior cloning (BC) methods often overfit to instance-specific geometry and appearance, limiting transfer to novel objects. We introduce KeyGen, a...
This paper investigates temporal neural networks for \mbox{end-effector} position \mbox{estimation} of an aerial continuum manipulator (ACM) operating under aerodynamic effects induced by the unmanned aerial vehicle (UAV). An experimental dataset is collected under stationary (\mbox{rotor-off}) and \mbox{free-hovering}...
Latent world models are typically trained to predict factual transitions, whereas model predictive control (MPC) must compare alternative actions from the same state. A model can therefore achieve low factual prediction error yet poorly distinguish candidate actions. We introduce AD-WM, an action-discriminative joint-e...
ART is a tool-injection framework that tunes any VLA model to leverage off-the-shelf tool modules for low-level vision, high-level affordance, and embodiment enhancement, and ART reduces the complexity of the action solution space through tool-use, which improves generalizability across different tasks but also reduces...
Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads. The post Offloaded inference for real-world physical AI robotics appeared first on Microsoft Research.
A Blog post by Amazon on Hugging Face
Director of CSAIL and MIT professor honored for her contributions to robotics, artificial intelligence, and autonomous systems.
Gemini Robotics ER 2 helps robots reason, collaborate, and solve real-world tasks. It represents a step change in video understanding, tool orchestration, and multi-robot collaboration for robotic applications.
From feet to fingertips — we are teaching robots intelligent whole-body control, fine dexterity, and teamwork to complete a broad range of complex tasks.
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