AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,183 papers
This work introduces FailBench, a benchmark for robot failure detection comprising 2,197 manipulation attempts across 14 public sources (12 real-world, 2 simulated), and shows that input-level intervention--spatially localizing and cropping outcome-relevant regions--improves the top detector by 2.4 percentage points wi...
Action-conditioned JEPA world models enable planning toward visually specified goals without reconstructing future pixels, yet latent prediction alone does not explicitly encourage the learned representations to retain information relevant to robotic control. We introduce an end-to-end JEPA world model that augments la...
This work introduces a data-driven morphology-control co-design framework that optimizes humanoid morphology for human-like movement and demonstrates that Bridge captures human motion data with superior fidelity, exhibiting exceptional performance across foundational locomotion, robust balance, and highly dynamic maneu...
AGC-VLN (Air-Ground Collaborative VLN), the first training-free baseline for air-ground collaborative VLN, establishes that training-free methods decompose navigation into VLM-based semantic reasoning and deterministic geometric execution, exposing a collaboration interface.
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It is argued that future world models should move beyond predicting likely futures toward identifying which futures matter, revising judgments through experience, and recognizing when to act, revise, sense, defer, or abstain.
Vision-language-action (VLA) models predict robot actions from visual observations and language instructions. These actions are defined in the robot's own 3D coordinate frame, yet most VLAs observe the scene in the camera frame, creating a frame mismatch between where the scene is observed and where actions are defined...
PILOT (Physical Inference for Latent Optimized Trajectories) bridges the gap between high-level physical condition evolution and low-level action trajectory generation within the Action Model, creating a structural bottleneck while weakening the predictive capability of world evolution modeling for action generation.
Faster-WAM, an instantiation of DoT for WAMs, which docks a single-layer action head onto a 30-layer video backbone, achieves competitive performance on LIBERO and RoboTwin 2.0 while demonstrating strong out-of-distribution generalization on LIBERO-Plus.
This synthesis shows that the open frontier of reasoning in driving agents lies in intermediate representations that can be grounded in the real world, coupled to real-time action, and verified under safety-critical systems.
The feasibility of dynamics-level co-optimization for the simulated soft robotic reservoirs considered here is demonstrated, with results demonstrating the feasibility of dynamics-level co-optimization for the simulated soft robotic reservoirs considered here.
A two-step deep reinforcement learning framework that jointly learns the maximal robust RAS set and the corresponding control policy and identifies the RAS set with high accuracy while outperforming baseline methods on RAS tasks.
Safe-Stop is proposed, a task-agnostic framework that pairs a learned stop policy with learned stoppability estimators, and yields decisions that are robust without sacrificing reactivity.
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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