AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Category
1,589 papers
Weave is presented, a unified framework for learning whole-body dexterous humanoid-object interaction from captured human demonstrations that converts captured human-object interactions into executable robot-object references through contact-aware retargeting and approach-motion completion.
The Neverwhere Benchmark Suite is developed, comprised of over sixty 3D Gaussian Splatting reconstructions of urban indoor and outdoor scenes, to encourage large-scale and reproducible robot evaluation by making it easier to create and integrate Gaussian splats-based reconstructions into simulated continuous testing se...
This work presents EMODY Flow, a lightweight flow-matching framework that attaches to a frozen Qwen-3 Omni model and reuses its internal Mimi audio-codecs to condition two parallel DiT generators - one for SMPL-X body pose, one for FLAME facial expressions.
Episode-Normalized Conformal Prediction (ENCP) is proposed, which rescales a nonconformity score by the policy's residual confidence and calibrates one maximum score per episode and can provide model-agnostic uncertainty estimates, which might be useful for determining when a VLN agent should defer to a more capable pr...
Reach audiences
Advertise in front of researchers, engineers, and readers.
The ability to generalize to novel motor sequences within the closed-loop control setting is demonstrated, improving on the generalization error of a task-input-conditioned multitask baseline by upto order of magnitude.
Precise manipulation of liquid droplets underpins lab-on-a-chip platforms for diagnostics, chemical synthesis, and biological assays. Yet autonomous droplet transport through confined geometries of varying complexity remains an open challenge. Droplets exhibit contact-angle hysteresis, deformability, and capillary pinn...
This work proposes a language-guided distillation framework for learning sensor-robust tactile representations and demonstrates that language-guided distillation enables scalable and hardware-agnostic tactile representation learning.
Embodied language grounding requires more than identifying the referent of an instruction: recovered semantics must also control the action an agent exposes. We study this missing link as a semantic-action gap, where instruction semantics are recoverable but weakly expressed in native continuous actions. We introduce S...
A task-distribution-aware synthesis framework in which the operating distribution $\rho(q)$ enters the design explicitly is developed, and Nondominated fronts show that preferred mass-radius pairs depend on declared engineering bounds.
Hip exoskeletons may improve recovery from unexpected gait perturbations, yet personalizing assistance remains difficult because balance is multidimensional and human-in-the-loop experiments are small-sample and noisy. We present a participant-specific composite balance cost that integrates seven biomechanical sub-metr...
Feature-fusion Multi-Agent Proximal Policy Optimization (FMAPPO) is proposed, which fuses 2D LiDAR measurements with task-specific state information to enable safe decentralized multi-robot task assignment and navigation and demonstrates that the learned decentralized policies can coordinate multiple robots to service...
This work proposes a self-supervised Track-Any-Point approach that learns from unlabeled surgical videos by establishing global pixel correspondences and inferring point trajectories through contrastive random walks, and achieves performance comparable to existing semi-supervised approaches while implicitly handling ti...
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
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
