Elon Musks Tesla Optimus 3.0 Humanoid Robot Academy (AI NEWS)

And in just a moment here, I’ll show you the dexterous demo with its hands. But peak torque of 400 newton meters, which is on the upper end of robot power, with a relatively small frame, 169 centimeters. And the running gate looks pretty realistic. And we can see here that it’s able to coordinate its entire body using its embodied AI. And in just a moment, we’ll see how it works with its hands. Look at the way that it can step up those stairs. That’s pretty impressive. And jump right back down. And here’s for the dexterous demo.

This is something that most humans can’t even do. Would be good to see this in one straight shot with no editing. And it catches the ball there. And then it gets into a real world work demo. So let’s see what this robot can do in the real world beyond just theatrics. Here we see it turning knobs and pressing buttons. And you can also use its vision language action model to pick up items, clear spaces, as well as handle high payload. Look at the way that it’s able to pull this cart while maintaining its balance.

And what’s interesting here is that they say that they’re going to provide a demo soon with some kind of brain computer interface, or at least that’s what it looks like from this preview. So would you hook up Tancong 3.0 to your brain? Meanwhile, a humanoid robot just completed a parkour course using only its onboard depth camera and a simple velocity command. This is thanks to perceptive human parkour or PHP, which is a brand new framework that enables the Unitree G1 humanoid to autonomously chain together dynamic movements, stepping over obstacles, climbing onto platforms, vaulting barriers and rolling off of ledges all in real time with no pre-planned paths.

And the challenge here goes way beyond stable walking because parkour demands explosive, highly dynamic movements that push the limits of balance and coordination. And it also requires that the robot is able to perceive its environment, decide which skill to use and execute that skill while preserving the fluidity of human motion. And while most humanoid systems can do one of these things, doing all of them together autonomously is the and this is where PHP comes in, solving this with a modular approach built on motion matching. So the system stores a library of retargeted human parkour movements as atomic skills, then uses nearest neighbor search in a feature space to compose these skills into long horizon kinematic trajectories.

And this allows the robot to smoothly transition between movements without the jerky handoffs typical of state machine approaches. So once these kinematic trajectories are composed, reinforcement learning expert policies are trained to track each motion. And in the end, it results in one unified policy that handles multiple skills, takes depth images as input and makes context aware decisions about which movement to execute based on obstacle geometry. And the results in the real world were striking as the G1 was successfully able to climb up obstacles that were 96% of its own height.

And it completed multi obstacle courses with closed loop adaptation, meaning that it adjusted its behavior in real time when researchers moved or perturbed obstacles mid run. And all this used onboard sensors with a discrete 2d velocity command with no external motion capture, no human in the loop, just perception decision making and execution as one continuous system. Plus unitry also showed off this demo of its cluster cooperative rapid scheduling system, which allows all of these robots to act as one coordinated system. So what else could this be used for beyond entertainment that would be useful, maybe construction or cleaning up dangerous site comment down below.

And finally, Google just released Gemini 3.1 Pro and it’s better than Gemini 3.0 Pro in multiple areas. First on Arc AGI to a test that evaluates a model’s ability to solve entirely new logic patterns that it’s never seen before. 3.1 Pro scored a 77.1%, which is more than double the reasoning performance of Gemini 3.0 Pro. In fact, this follows a recent update to Gemini 3 deep think, which targeted scientific research and engineering challenges. But Gemini 3.1 Pro is the upgraded core intelligence underlying those capabilities now shipping across Google’s consumer and developer products.

And already Gemini 3.1 Pro is available in the Gemini API and Google AI Studio, Gemini CLI, Google’s agentic development platform, anti gravity, Android Studio, vertex AI, Gemini enterprise, the Gemini app and notebook LM. On top of this, higher usage limits are rolling out for Google AI Pro and ultra subscribers. So altogether, Google’s 3.1 Pro is being positioned for tasks where simple answers aren’t enough, such as with synthesizing data into unified views, generating visual explanations of complex topics, or supporting creative projects that require multi step reasoning. And while it’s currently in preview, Google says they’re validating updates and pushing further on agentic workflows before general availability.

Anyways, like and subscribe and check out this video here for more of the latest in AI and robotics news. [tr:trw].