AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,589 papers
Flow-matching policies are promising for imitation learning because they model complex multimodal action distributions. However, their stochasticity is largely passive: repeated sampling may yield diverse behaviors, but users cannot directly choose among valid continuations from the same state. We propose Source-Lifted...
This study presents an automation framework for robotic RSL that leverages a physics-informed machine learning (PIML) model to precisely estimate the source location, regardless of measurement paths, in unknown environments.
Heterogeneous memory and diversity-aware replay provide an effective basis for continual aerial VPR in mapped operating areas and introduce DBS-Hybrid, which combines prototype-based diversity trimming with representative-first feature-space coverage.
This paper adapts set difference captioning to autonomous driving by focusing on object-centric patches derived from object detection, which simplifies aggregation and enables attribution of differences to specific object instances or categories and introduces a new benchmark, AD-Diff Bench.
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Evidence-Gated Regularization (EGR) is proposed, a modality-agnostic training objective that introduces zero inference-time overhead and derives a per-frame and per-sensor task-relevance signal to gate two state-conditional consistency objectives: invariance on low-evidence sensors, and single-sensor sufficiency on hig...
Wildlife field operations demand efficient parallel deployment methods to identify and interact with specific individuals, enabling simultaneous collective behavioral analysis, and health and safety interventions. Previous robotics solutions approach the problem from the herd perspective, or are manually operated and l...
Empirical findings show that, despite increased model scale and enhanced general reasoning capabilities, successive VLM generations do not consistently achieve better driving performance, and highlight the need for domain-specific adaptation to improve the safety of VLM-based autonomous driving systems.
This paper presents a low-cost, open experimental platform for research in end-to-end autonomous driving with miniature Ackermann vehicles. The platform combines a physical vehicle, a printed urban track, data collection tools, trajectory registration, and a Webots digital twin, enabling controlled experiments that con...
The results indicate that decoupling physical grasp synthesis from task-dependent understanding provides a scalable paradigm for robotic grasping, allowing future advances in foundation models to be directly translated into improved grasp capabilities without redesigning or retraining the underlying grasp policy.
Reinforcement learning from human feedback (RLHF) has emerged as a powerful yet sample-inefficient approach for learning reward models from human preferences, making active learning a critical component in synthesizing informative preference queries. However, effective uncertainty quantification required for active lea...
This work treats catalogue photography as a useful cold start rather than a deployment-ready training domain, and provides empirical baselines and an evaluation protocol for catalogue-to-field transfer in precision tool manufacturing.
GraFT is introduced, a training-free framework that supplies the missing 3D structure through a compact, easily maintained 3D scene graph (3DSG) and provides three spatial reasoning capabilities: deterministic geometry through symbolic tools, allocentric layout through a bird's-eye-view (BEV) rendering, and visual-attr...
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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