AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Category
1,589 papers
A decentralized framework that integrates predictive shielding with model-based finite horizon Q-learning is proposed, which allows agents to safely adapt their pre-trained policies during deployment and introduces a communication- free protocol for conflict resolution.
Contact-rich assembly remains challenging because it requires submillimeter spatial accuracy and reliable interpretation of forces during sustained contact. Although simulation-based reinforcement learning offers a scalable training paradigm, discrepancies in visual observations, contact dynamics, and force/torque (F/T...
Autonomous driving relies on High Definition (HD) maps for safe navigation. Traditional HD maps construction is costly in hardware, data and human resources, which together with its update limitations hinders scalability. Recent works have proposed online alternatives for HD vectorized mapping from onboard sensors. How...
Robot foundation models are trained and evaluated predominantly in English, and robot demonstration corpora do not exist for most languages. We study the addition of Greek to an open vision-language-action stack using only machine-rephrased instructions and no architecture changes. The main challenge is measurement rat...
Reach audiences
Advertise in front of researchers, engineers, and readers.
D3ARC is introduced, an asynchronous distributed hierarchical framework for time-aware and reliable wildfire detection that introduces a forward-looking capability that allows agents to anticipate the future by evaluating candidate strategies before execution.
PV-WM, a history-only world model over structured post-perception tracks, recurrently advances pedestrian root motion, 15-joint articulation, and learned vehicle states within a synchronized heterogeneous state while preserving type-specific pedestrian and vehicle dynamics.
This work studies whether large language models (LLMs) can fill this gap by generating Parametric Signal Temporal Logic (PSTL) specifications that are subsequently used for policy learning, and instantiate the parameters of the generated PSTL templates using expert trajectories and retain only specifications that are c...
MEMOBench is presented, a benchmark for process level memory evaluation in robotic manipulation and provides a diagnostic evaluation suite and training supervision for memory grounded robotic policies.
Reinforcement learning has become the leading paradigm in legged locomotion, enabling complex behaviors from backflips to parkour through massively parallel simulation. Under PPO's non-stationarity, shallow networks remain the de facto architecture, supported by carefully staged curricula and environments, yet the repr...
A noisy-space action-value (Q-)function that assigns values to diffusion latents through the distribution of executed actions induced by the denoising process is introduced, which admits a precise semantic interpretation and derives a noisy-space policy gradient (NSPG) that optimizes noisy latents using only clean acti...
Behavior-cloned visuomotor policies can remain accurate near their training distribution yet fail when object position and camera viewpoint change together. A successful reference trajectory contains the geometry needed to transfer the same interaction, but the policy must align that geometry with the current scene and...
The generation of safety-critical traffic scenarios is essential for training and evaluating autonomous vehicles. Prior approaches typically perturb the trajectories of existing agents in a traffic scenario using simplified adversarial objectives to induce safety-critical interactions, which can limit the plausibility...
A Blog post by Ai2 on Hugging Face
A new system identifies and corrects unreliable object descriptions stored in a robot’s memory, improving the robot’s ability to interact with its environment.
Discover Gemini 4 Argon for robotics: leading the ERQA benchmark with physical reasoning, zero-shot control, Real2Sim, and agentic workflows.
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
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
