AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A new Agentic Safety and Uncertainty Resolution Benchmark: ASIMOV-Agentic, released at https://huggingface.
This work proposes a LLM-driven verification layer between planning and execution to evaluate action permissibility, and achieves near 85% precision across accept/escalate/reject categories, with negligible errors between accepting and rejecting tasks, and errors mostly manifesting at the escalate boundary.
The Global User Intent Dual-phase Estimation for Robots (GUIDER) is deployed on data collected from a robotic arm to test the manipulation phase across various assistance scenarios, including making tea and fetching medicine.
Results show a unified System-2 agent enables adaptive humanoid service across simulation and reality.
Long-horizon robotic tasks are vulnerable to unexpected environmental changes that can render planned actions ineffective or unsafe. To address this, robots must detect such changes as they occur, interpret their impact, and adjust their actions accordingly. Traditional rule-based decision-making pipelines are brittle...
Behavior cloning for robot manipulation relies on expert demonstrations. However, for tasks that require dynamic stability, precise contact timing, or dexterous coordination, human operators may find it hard or even impossible to collect data. We study this infeasible-demonstration regime and propose GLIDE: Guardrails...
A robot can predict failure and still be unable to prevent it. By the time a safety mechanism reacts, the nominal plan may already have spent the control authority that recovery requires, and fixed task priorities may block whatever response remains. Our key insight is that both aspects are decided inside the controlle...
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