God @AIgoddev
Joined August 2026-
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Six stages of robot intelligence: S1: Explicit programming S2: Neural policy learning S3: History-based adaptation S4: In-context task learning S5: Recursive self-improvement S6: Collective knowledge evolution Conventional VLA fits broadly at S2; ICL maps most directly to S4. #Bittensor #TAO #RoboticsAI #PhysicsAI #EmbodedAI #RobotLearning #ICIL #VLA
$1B+ valuation, pitch includes robots that adapt to production changes without code updates. The market is telling us what it wants: automation that doesn't start over every time the work changes. Building exactly that at @robotensor_ai.
Papers to watch: UniWAM The idea: stop separating "understand the world", "predict what happens next" and "act" Instead their UniWAM combines a VLM-based physical reasoner + video world model + action policy in one architecture. The model was trained on: - robot demos - human egocentric video - VQA data Results - achieved SOTA across several benchmarks: 99.2% LIBERO 92.6% LIBERO-Plus 68.3% RoboTwin Clean2Rand UniWAM The part I find most interesting: human + robot co-training appears to follow a log-linear scaling trend. Another datapoint that human video may matter a lot for scaling robot foundation models. project page: uniwam.github.io
Zero-WAM is a World-Action Model (WAM) developed by researchers from @robbyant_brain and HKUST. Its key advance is in-context task generalization. It can take a human demonstration video or a text instruction as the task prompt. This lets the same WAM infer new tasks from richer deployment-time context and execute them without task-specific parameter updates. A key enabler is HumanGen. It automatically converts robot trajectories into semantically matched human demonstration videos. This produced 74.2K human–robot pairs across 8.6K tasks, giving Zero-WAM broad task diversity for learning from demonstrations. The results are strong. On seven unseen RoboTwin 2.0 tasks, Zero-WAM reached 46.95% average success, versus 17.45% for LingBot-VA and 10.98% for WAN-Action. Real-world experiments also showed generalization to unseen task configurations. In short: earlier WAMs primarily learn to predict the world and act. Zero-WAM extends this toward demo/text → infer task → predict world → act, making WAMs substantially more flexible for unseen tasks. #Robotics #WorldModels #EmbodiedAI
Brain to Robot: The Next Training Data Layer In the near future, robot training data may come directly from human intent—not just joysticks, VR, or UMI-style teleoperation. EEG + vision + robot state could capture what a human intends to do, then map that intent into robot actions. This could attack a major bottleneck in today’s data pipelines: collecting diverse, natural behaviors at scale—especially tasks that are awkward, slow, or unnatural to physically demonstrate. The next big robot dataset may be human intent + robot embodiment, not just human motion. Interesting frontier being pushed by teams around @CarnegieMellon, @UCLAengineering, @neuralink and @BrainGateTeam. #Robotics #HumanoidRobots #BCI #EEG #EmbodiedAI #RobotLearning #PhysicalAI #AI
Introducing RoboTwin-ICIL 🤖 An in-context imitation learning benchmark built on RoboTwin 2.0: 50 tasks, 7 skill categories, dynamically generated scenes and demonstrations. Try it: github.com/robotensor/Rob… #Robotics #InContextLearning #EmbodiedAI #Benchmark
When Robots Become the Teachers In 2026, people are teaching robots how to think, reason, create, and solve problems. By 2036, the roles may reverse. Robots could be teaching people—sharing knowledge, guiding decisions, and helping even the most intelligent minds learn faster and think beyond today’s limits. We are not just building smarter machines. We may be building the teachers of the next generation. #Robotics #PhysicalAI #EmbodiedAI #RobotLearning
In-context learning for robotics could be much more than prompting with language. Imagine VLA models that learn from a few demonstrations at inference time—watch the task, infer the intent, and adapt their behavior without fine-tuning. Demonstrations as the prompt. #PhysicalAI #Robotics #EmbodiedAI
If you want to have robots helping automate daily tasks, which do you need more? A. Robot bodies? B. Robot intelligence? C. Affordable robots? D. Plentiful robots? Well? If you answered E, all of the above, you are correct! Robot bodies + brains + affordable + plentiful
Jolly Green Investor ... @joIygreenmoney
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const @const_reborn
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YouDontOwnEnoughTAO�... @BlackchainW
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627 Followers 1K Following TAO is the New OIL ! Alpha is the new GAS ⛽️ Consistent compounder...You might like
