Unitree G1 + Reborn AI Roboverse 3 In 1 Demo Humanoid Robot Boxing Upgrade

Unitree Robotics and Reborn have partnered to create Roboverse, a platform that helps robots learn human tasks faster and more efficiently. This platform includes a simulation environment for safe practice, a dataset for diverse scenario training, and benchmarks for performance comparison. The partnership also encourages global developers to contribute to the open-source robotics community. Additionally, new open-source robots have been introduced by Hugging Face, and a self-improving AI called the Darwin Girdle Machine has been unveiled, which could surpass handcrafted AI systems in the future.

I’m AnaNews, and Unitree Robotics just took Android Learning closer to AGI by announcing a partnership with Reborn to deploy a 3-in-1 platform on its G1 humanoid, plus future models. But what does it do, and how smart is it now? To start, the platform is called Roboverse, and it finally streamlines the development of next-generation robot policies so that humanoids can learn to do anything that a human can, but now more easily and much faster. And while Roboverse will help to upgrade the G1’s abilities while building an open-source robotics community, Unitree is also giving birth to a separate multi-billion dollar industry that doesn’t exist yet, so how can you get in on the ground floor? Before we get into that, Roboverse’s 3-in-1 platform integrates the following critical features.

First, it includes a powerful simulation platform where robots can safely practice tasks in virtual environments, which reduces the need for costly real-world testing. Secondly, it offers a rich synthetic dataset generated from diverse simulated scenarios, which helps to train robots on a wider range of situations that they may encounter in the real world. And thirdly, the framework provides unified benchmarks that allow developers to consistently measure and compare robot performance across different tasks and learning models. And by combining these three elements into a single system, Roboverse accelerates robot learning while improving their safety, efficiency, and scalability.

And on top of this, Roboverse also supports multiple simulators and robotic embodiments to make possible seamless transitions across diverse environments. And all this put together provides for incredible results, with Roboverse accelerating robot policy training by up to 30 times. And this is huge for researchers because it significantly speeds up the time of development for intelligent and adaptable robot behaviors. Plus, this efficiency is also poised to drive advancements in unitry’s own humanoid robots, enabling them to tackle even more general tasks and behaviors at work and at home in their newer models. But beyond tech integrations, Reborn and unitry are also partnering to co-host hackathons with both companies inviting developers from around the world to contribute to the open-source robotics ecosystem.

And this deployment of Roboverse will inevitably yield smarter robots that are more generally capable to entertain us. And this is why, alongside unitry’s Roboverse partnership, the company is also breaking new ground with its boxing bots designed for dynamic robotic combat. And they’re powered by a motion capture training system where the boxing bots are already showing some pretty smooth punching, hooks, and front kicks against humans and robots. Additionally, the G1’s AI control systems are constantly optimizing to maintain balance, allowing them to recover more quickly from being knocked down so they can continue fighting.

In fact, the G1 boxing bots are always learning, and they’re picking up new skills with their continuous algorithms. Users can even control them via motion sensors, controllers, and voice commands to make them easier to operate. And in terms of unitry’s Roboverse collaboration, these boxing bots are likely to benefit from the same simulation and learning tools that speed up policy training, and this will make them even sharper in future boxing and combat scenarios. And while more details on their deployment are yet to be released, it’s apparent that unitry is moving towards blending practical robotics with engaging, high-performance designs that fit different use cases.

Plus, because unitry’s open-source community is growing so fast, it’s very likely that global developers will further refine these bots with time to add new potential in competitive and creative applications. But there are also two new open-source robots that were just unveiled by Hugging Face, starting with their first robot, Hope Jr. And this is a full-size humanoid robot with 66 actuated degrees of freedom, giving it the ability to use all of its limbs just like a human would. But the real news is that the Hope Jr. is supposed to cost just $3,000 according to its bill of materials.

Next, their second robot is the Reaching Mini, which is a compact desktop unit that’s supposed to cost between $250 to $300. And this has the ability to move its head, speak, listen, plus serve as a testing platform for other AI applications. And both of these robots are fully open-source, allowing developers to assemble, modify, and study their designs with the added benefits of both affordability and transparency when compared to other proprietary systems. But when it comes to the release dates, the shipping timeline is by this year’s end, with the waitlist now being open.

And Hugging Face has been steadily advancing in robotics, launching its LeRobot in 2024 and its collection of open AI models, datasets, and tools for building robotic systems. But there’s another robot at ETH Zurich that’s combining an arm and legs with the latest AI to now play badminton. And this is possible thanks to model-free reinforcement learning, meaning the robot leverages all of its joints and onboard sensors to chase down the shuttlecock and smash it. And what’s more is that the control policy, developed entirely in simulation, was deployed directly on the robot’s hardware without any further tuning, which showcases its definitive real-world performance.

And the result is that the robot runs to the interception point, swings at the expected trajectory of the shuttlecock, and it may even tilt its base to further optimize its hits. Then using what’s called asymmetric actor-critic architecture, the AI’s training process incorporates privileged simulation data to further improve its learning efficiency. And what’s interesting is that in experiments, the robot exhibited two distinct approaches, with its gate adapting dynamically where, for short distances, it maintains prolonged foot contact, and it lifts its feet only to reorient for optimal hits. But for farther targets, it instead adopts a kind of galloping gate, where the robot adjusts its foot contacts to increase its impact and recovery time.

And when it comes to the latest in AI-driven search, Perplexity just introduced Perplexity Labs, which now lets users create reports, spreadsheets, dashboards, and interactive web apps. And as of now, Labs uses AI for research, analysis, and data visualization. But unlike Perplexity’s core search function, Labs is also designed for tasks requiring deeper processing, often taking up to 10 minutes or more. And this is because Labs uses web search, code execution, and chart and image creation to generate structured documents and apply formulas. Next, all of Labs outputs, including charts, images, and code, are then organized in a way of previewing and downloading.

In fact, Labs is part of Perplexity’s broader strategy to expand beyond search, with recent moves including previewing its web browser, which it calls Comet, as well as acquiring read.vc, which is a professional social network. At the same time, a breakthrough self-improving AI was just unveiled, and it’s called the Darwin Girdle Machine, or DGM. But there’s a twist, because unlike the original Girdle Machine, which required impractical mathematical proofs to ensure code changes were indeed beneficial, the DGM takes a much better approach. And it works by combining principles of open-ended algorithms with foundation models to propose and test code improvements, where these improvements are evaluated empirically, allowing the system to search for and develop a diverse library of high-performing AI agents.

In fact, results show that DGMs improve their performance as more computational resources are provided, which aligns with the broader trend in AI research, where systems that rely on learning consistently outperform manually designed ones. Furthermore, DGM’s ability to iteratively enhance itself suggests it could eventually surpass handcrafted AI systems, marking a pivotal step towards creating AIs that are capable of indefinite learning. But if you want to know more about the latest in AI news, make sure to like and subscribe and click this video here. [tr:trw].