AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,589 papers
PDRVLA is introduced, whose lightweight Physical Dynamics Module extracts physical tokens from vision--language features to condition action generation without online video generation, andlations show improved future prediction with world-model supervision and evaluate how subtask conditioning, future object-pose super...
Tactile sensation is essential for contact-rich manipulation tasks. It provides direct feedback on object geometry, surface properties, and interaction forces, enhancing perception and enabling fine-grained control. An inherent limitation of tactile sensors is that readings are available only when an object is touched....
NablaEngine and $\nabla$Mimic are released as open-source infrastructure for first-order humanoid policy learning and it is shown that exact gradients of the simulator itself do better, once two obstacles are removed.
SACQ is introduced, an ultra- low-bit weight-only vector-codebook quantization framework for VLAs that combines structure-aware codebook fitting with role-specific quadratic metrics that reflect input statistics and downstream sensitivity.
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Mechanistic interpretability to E2E driving is applied by training Sparse Autoencoders on the internal activations of a simple, single-camera end-to-end path planning model, revealing interpretable structure aligned with route intent, vehicle speed, and braking behavior.
AquaProbe separates newly introduced divergence from that accumulated through a frozen vision encoder using paired clean and physically rendered images and allocates a fixed LoRA budget according to the measured profile.
FOCUS is introduced, which combines local optimal-transport alignment with short-horizon differentiable actor-critic learning and improves sample efficiency and training stability over adversarial imitation-from-observation methods and prior differentiable imitation baselines.
This work proposes a specification-aware action distribution optimization framework that enforces a broad class of Signal Temporal Logic constraints during execution of a pretrained robotics foundation model without modifying its parameters.
Self-driving laboratories (SDLs) accelerate scientific discovery by automating experimental execution and optimization. AI agents can increasingly propose new experiments, but existing SDLs can only run the procedures they were built for. Adapting them to a new experiment still requires extensive manual engineering, wh...
A universal controller for any robot morphology would greatly improve computational and data efficiency. Steps have been made towards such multi-robot control by utilizing contextual information about the properties of individual robots and exploiting their modular structure in the architecture of deep reinforcement le...
This work introduces ID-WM (Implicit Dynamics World Models), a hierarchical world model that decouples state-space reachability from local control and yields a 135% performance gain over single-level world model MPC, particularly in complex, unseen configurations for long-horizon tasks.
RoboArchitect shows that harness design can be automated, offering a strong alternative to finetuning on a novel setup, and its design can be effectively automated and optimized without weight updates.
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
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