Updated
Updated
AINVIDIA highlighted the developers who won the Cosmos Cookoff and how they used Cosmos Reason 2 to build projects spanning disaster-response drones, explainable visual AI, and intelligent security systems. The post links to a YouTube showcase and a LinkedIn recap of the event.
AIJim Fan announced the open-source release of CaP-X, an agentic robotics framework in which LLM-driven agents control robot arms and humanoids through perception and actuation APIs. The release includes CaP-Gym with 187 manipulation tasks across RoboSuite, LIBERO-PRO, and BEHAVIOR, and CaP-Bench, which evaluates 12 frontier LLMs and VLMs across 8 tiers. The post also reports that a 7B open-source model rose from 20% to 72% success after 50 RL training iterations, with synthesized programs transferring to real robots.
AIJim Fan argues that behavior cloning directly from humans, following EgoScale and its dexterity scaling law, has become the way to move past teleoperation. He says 2026 will focus on scaling robot learning without robots. The post is cited alongside EgoVerse, an ecosystem for egocentric human data with 1300+ hours across 240 scenes and 2000+ tasks.
AIBAAI has released RoboBrain-Dex, a dexterous manipulation model for embodied intelligence trained on large-scale, diverse human egocentric data rather than massive robot teleoperation datasets. The company says this approach yields strong generalization, marking a shift from small-data, weakly generalizing methods toward big-data robotic manipulation. The code has been open-sourced on GitHub.
AIResearchers trained a humanoid with 22-DoF dexterous hands mainly on over 20,000 hours of egocentric human video, with no robot in the loop, to perform tasks such as assembling model cars and folding shirts. They report a log-linear scaling law (R² = 0.998) between human video volume and action prediction loss, and state that this loss predicts real-robot success rate. The recipe, called EgoScale, pre-trains GR00T N1.5 on the video, adds only 4 hours of robot play data, and reports a 54% gain over training from scratch across five dexterous tasks.
AINVIDIA researchers trained SONIC, a 42M-parameter transformer, to control a humanoid robot's whole body using motion tracking on over 100M mocap frames. After three days of training in simulation, the policy transferred zero-shot to the real G1 robot and reported a 100% success rate across 50 real-world motion sequences. One policy supports VR teleoperation, webcam human video, text prompts, music, and GR00T N1.5 VLA integration with 95% success on mobile tasks, and the code and checkpoints are open-sourced.
AIJim Fan announced DreamDojo, an open-source interactive world model that takes robot motor controls and generates future frames in pixels. It is pre-trained on 44K hours of human egocentric video using latent actions, then post-trained onto specific robot hardware, and a real-time version runs at 10 FPS for live teleoperation, policy evaluation, and model-based planning. The author reports a +17% real-world success gain on a fruit packing task, and weights, code, datasets, and the whitepaper are released.
AIJim Fan posted that greatness arises when non-consensus peaks, a brief remark with no further detail. The post was a reply-style comment to a Sara Ormous post about divergent views on how robotics will develop being a major AI opportunity.
AIBAAI released RoboBrain 2.5, an embodied AI model that turns 2D scene understanding into actionable 3D trajectories and provides dense temporal value estimates for real-time progress feedback on long-horizon tasks. The post says it achieves SOTA across multiple spatial and temporal reasoning benchmarks, though it names no specific scores. Project page, paper, GitHub code, and model weights are linked.
AIBAAI's RoboCOIN is a bimanual robot dataset with more than 180,000 trajectories across 421 tasks, collected from 15 robot platforms in 16 real-world scenarios. Its three-tier annotations at trajectory, segment, and frame levels help robots learn both what to do and how to do it. The post says integrating these annotations raised success rates on complex tasks by up to 50% for models such as π₀.