For instance, in a practical can-sorting task, a policy trained with DexMimicGen data hit a remarkable 90% success rate, compared to a stark 0% when using only human demonstrations. This capability significantly reduces the traditional costs and effort of data collection while enhancing the training process for robotic manipulation. Number two, the three task categories. DexMimicGen builds upon NVIDIA’s MimicGen framework, but it doesn’t stop there. It also extends to the intricate world of bi-manual tasks, classifying them into three essential categories, parallel, coordination, and sequential. By incorporating asynchronous actions and synchronised execution, it ensures that dual arm movements are efficient and fluid, with the system having been tested across nine simulated environments, with each utilising tele-operated demonstrations to create a substantial, adaptable data set.

And the outcome is that policies trained on DexMimicGen data outperformed those that relied solely on initial human inputs, proving that more comprehensive training data can elevate the success rates of robotic policies. And finally, there’s a secret weapon. Number three, transformation. One of the standout features of DexMimicGen is its intelligent approach to generating varied data through transformation schemes. Unlike basic methods that add noise to existing demonstrations, DexMimicGen surpasses these by producing high-quality training data that enhances adaptability. In fact, the system leverages the diffusion policy architecture which is noted for outperforming traditional methods in learning results.

And this is particularly important when training robots to navigate different task setups, because it allows for adaptable and robust policy performance. And as for its real-world success rate, the system achieved a score of 90% with a can-sorting task, speaking volumes about the system’s real-world effectiveness. And as for the future, there’s still a ton of potential around DexMimicGen’s growth and optimization. Some of these upcoming enhancements might include more sophisticated subtask segmentation and the exploration of different imitation learning algorithms. Additionally, testing across more varied tasks and environments could further generalize its applications, while integrating active exploration and online learning might also amplify its adaptability and efficiency.

Nevertheless, DexMimicGen already offers a robust, scalable solution for one of the toughest challenges in robotic training, data acquisition, and by reducing reliance on extensive human input, it accelerates the development of dexterous robots, setting a new standard for scalability in the field. And in another example of Android development, Booster Robotics just demoed their Booster T1 humanoid robot doing several tasks, with its open-source platform being aimed at developers and researchers with a focus on flexibility and adaptability across a variety of applications. As for its specs, the T1 stands at 118 cm and weighs 30 kg, featuring a design that’s both lightweight and durable.

This combination allows the robot to perform complex tasks such as playing sports or executing intricate maneuvers like Kung Fu with the robot’s strength, allowing it to handle a payload of up to 5 kg. Plus, it can transition smoothly from a prone position to standing to further demo its onboard motion control system. Furthermore, because it’s equipped with a software development kit, the T1 also offers developers the opportunity to create custom applications suited to their specific needs. In fact, its connectivity via Bluetooth to mobile apps allows for real-time control and feedback, adding a layer of versatility that enhances experimentation and development.

And as for power, the Booster T1’s runtime per charge is 1.5 hours, giving it a maximum walking speed of 3.5 km per hour and powering all of its 23 degrees of freedom so it can autonomously navigate across dynamic environments. What’s more is that Booster Robotics has secured significant investment to expand the production and market reach of the T1, with the company aiming to foster collaboration with developers worldwide to make humanoid robots more accessible and customizable. But researchers also just unveiled another breakthrough AI framework to significantly enhance video and scene generation capabilities, and it’s divided into three key components, with each offering unique advancements to the field.

The first component is the ST Director for Controllable Video Generation, which decomposes spatial and temporal parameters in video diffusion models. By utilizing dimension-aware LoRa on specially curated datasets, it allows for precise control over video generation, enabling creators to fine-tune content with unprecedented accuracy. Next, 3D Scene Generation with S Director offers new possibilities by reconstructing high-quality 3D scenes from a single view of video frames, which is an advancement that could transform applications in virtual reality and gaming by providing more immersive and realistic environments. Finally, 4D Scene Generation with ST Director begins with a single image to create a temporal variant video using T Director.

A keyframe is selected to produce a spatial variant reference video, guiding the generation of per-frame spatial variant videos. These are then combined into multi-view videos and refined through T Director to optimize a consistent 4D scene. Altogether, this framework promises to transform digital content creation by enhancing realism and control in video and scene generation, opening up new avenues for innovation in various industries. And finally, Cling just previewed the upcoming release of its new character consistency feature, now in beta testing with early access users and a wider rollout being expected soon. This feature enables users to train their own video models.

By using 10 to 30 videos of the same character, each at least 10 seconds long, users can create a custom character model. Once trained, characters can be generated in images or videos using the App mention feature. Users can begin by creating a character image with text-to-image models like Cling or Flux. Next, they produce 10 face-focused videos using Cling’s image-to-video capability. These videos are then submitted to Cling’s character training function. Once trained, the character model is ready to be used with the App mention feature for content creation, with its results showing impressive facial consistency, though clothing details may vary.

Currently, the system supports only single-character training, but Cling’s new feature is opening up exciting possibilities for personalized video content creation in the near future. If you enjoyed this video, please like, share, and subscribe!
[tr:trw].