AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,589 papers
MINT is proposed, which first trains VLA policies to remain functional under missing visual inputs, and selectively supplements missing observations using optical-flow extrapolation or an action-conditioned world model, and withdraws predicted views when they become unreliable.
RoboFL, which instantiates MoSAIC (Mixture of Slotted Adapters) for federated world-action learning, is presented, as it outperforms centralized PEFT InternVLA-A1 by 12.23% on the Franka arm, while reducing per-round client communication by up to 86.81% relative to MoE-based federated VLA baselines.
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.
This work introduces CoHuB (Collaborative Multi-Humanoid Benchmark), a simulation benchmark for multi-humanoid collaboration under egocentric visual observations and provides a foundation for developing and evaluating multi-humanoid collaboration policies.
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This work theoretically and empirically demonstrate that policy gradient methods can successfully solve stabilization tasks without first-order reward terms, and confirms that reward functions must be zeroth-order complete over goal-relevant coordinates, while the first-order state remains necessary in the policy obser...
World Action Models (WAMs) enable future-aware control by jointly modeling actions and environment dynamics. However, iterative diffusion or flow inference incurs substantial denoising latency. Prior inference state offers a natural opportunity for acceleration, yet changing planning contexts, observations, and interme...
This work introduces LRC-JEPA, a lightweight end-to-end world model that routes information into a compact predictive latent and learned-query residual-context embeddings and shows that the resulting representation is sufficient, minimal, nuisance-invariant, and disentangled.
A framework termed Predictive Semantic Safety (PSS), which connects visual physical reasoning to backup-based safety filtering and derives input-affine constraints for minimally modifying the nominal input while preserving backup feasibility under the robot dynamics and input limits.
World models offer a powerful substrate for safety reasoning in high-dimensional robotic systems, but they are also fallible: their predictions can be biased, miscalibrated, or confidently wrong. This creates a central challenge for latent-space safety filters, which often learn Hamilton-Jacobi safety value functions o...
This work proposes a Bayesian framework that treats clarification as an active learning problem over grounded Signal Temporal Logic task specifications and uses LLMs to initialize candidate formal specifications and translate informative contrasts into natural-language clarification questions, while Bayesian optimizati...
SAGE (Symbolic Action-Gating and Editing), a single-LLM planner built from two lightweight mechanisms: a domain-agnostic symbolic gate that blocks precondition-violating actions with typed reasons as a runtime safety monitor, and a local edit that regenerates only the failed sub-goal's suffix, keeping completed and unt...
On-policy distillation (OPD) for WAMs and WAM-OPD are explored and WAM-OPD is introduced, which improves target-task performance while retaining near-initial performance on tasks excluded from adaptation.
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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