Tag
robotics
Robotics here focuses on the methods that let machines act reliably in the physical world: perception, control, simulation, and planning. Topics include world models, hierarchical planning, sparse-demonstration learning, and deformable-object simulation, all aimed at better long-horizon control with less compute.
8 articles
Framework’s fund turns tokenization into AI financing
Framework’s $400M fund backs tokenization and stablecoins as financing rails for AI, robotics, and energy infrastructure.
World-action models are becoming robotics’ second bet
4 ways world-action models are changing robot policy design, from video priors to action prediction and hybrid control.
InSight lets VLAs learn new skills on their own
InSight makes vision-language-action policies learn new manipulation skills without human demos of those target tasks.
AutoDex automates dexterous grasp data collection
AutoDex automates real-world dexterous grasp trials and labels physical outcomes without human intervention.
Anthropic’s robodog test shows physical agentic AI is arriving
Anthropic’s Project Fetch Phase Two shows Claude can already outperform humans on limited robot tasks without help.
VERITAS lets robots verify and improve at runtime
VERITAS uses a visual verifier to steer robot policies at inference time and improve them from verified self-generated rollouts.
SIM1 turns sparse demos into deformable-world data
SIM1 grounds deformable-object simulation in real scenes, then scales sparse demos into synthetic training data for data-efficient robot policy learning.
Hierarchical Planning Cuts World-Model Search Cost
A hierarchical latent world-model planner improves long-horizon control and cuts planning compute, with zero-shot gains on real robots.