AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,183 papers
Context-dependent consequence modelling provides a principled mechanism for allocating conservatism by physical risk in autonomous robot navigation failure prediction as consequence-sensitive forecasting.
The proposed situation identification approach enables efficient sensor data management by prioritizing relevant data to the current traffic situation by identifying the current traffic situation using Bird's-Eye-View images.
This work formalises CDPs using a goal-conditioned policy divergence measure and incorporates them into a Receding-Horizon Planner that explores future action sequences while optimizing a cost function balancing task progress and information gain.
Ensuring the safety of autonomous driving is a critical challenge. Scenario-based testing is a systematic process used to validate Autonomous Driving Systems (ADSs), but it remains a fragmented modular pipeline in which scenario generation, retrieval, modification, ADS execution, and results analysis are performed by s...
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Reward-free latent world models plan by scoring candidate actions with distances in a frozen latent space: an action is preferred if its predicted future embedding lands closer to the goal embedding. This silently assumes that latent closeness is action-rankable, i.e., that ordering candidates by latent distance agrees...
Team communication breakdowns represent a contributor to patient safety risks within action teams-defined as interdependent groups of specialized people who perform coordinated work under high workload, time pressure, and uncertainty. Approximately 70% of such instances lead to adverse patient outcomes amid intense tim...
This work shows that paraphrase robustness is a core requirement for reliable VLM-based reward modeling in robotics and introduces ROBORMBENCH, a benchmark with 2,390 real-robot trajectories, ground-truth progress labels, and 21,673 verified paraphrases spanning lexical, syntactic, and action-goal rewrites.
This work proposes SCRIPT, a scalable diffusion policy with a multi-stage training framework for language-driven physics-based humanoid control, and introduces a nonlinear history conditioning mechanism, which preserves the dense recent context and samples increasingly sparse cues from long-term history.
Visual reinforcement learning is appealing for robotics but expensive. Off-policy methods are sample-efficient yet slow while on-policy methods parallelize well but waste samples. Recent work has shown that off-policy methods can train faster than on-policy methods in wall-clock time for state-based control. Extending...
A resilient communication-aware remote robotic control framework based on coupled control and wireless Joint Embedding Predictive Architecture (JEPA) world models that jointly capture robot dynamics and wireless channel evolution from visual observations and a combination of raw and structured radio frequency (RF) repr...
Octopus, a hardware onboarding framework in which a coding agent, rather than a shipped integration, is the runtime that produces the required infrastructure, is presented.
The trustworthy object detection capabilities of a novel Kolmogorov-Arnold network framework are examined here, and it offers a powerful tool for transparent and practical perception component for autonomous and multimodal artificial intelligence applications.
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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