The key innovation behind cross-former lies in its ability to process and learn from diverse robotics data without requiring manual adjustments or standardization. This approach leverages the transformer architecture, which has been highly successful in natural language processing tasks. The transformer’s ability to analyze entire sequences of data makes it well-suited for handling the varied input formats from different robot embodiments. Furthermore, the research team explains that the challenge of creating a generalized robotic control policy is similar to that faced by large language models. Just as language models must identify patterns in sentences with different lengths and structures, cross-former needs to recognize patterns in robotic data that vary significantly depending on the robot’s physical configuration and sensory inputs.

And that’s why the development of cross-former builds upon recent efforts to pool robotics data from various sources, such as the OpenX embodiment and DROID datasets, in order to achieve positive transfer, where skills learned from one task or robot type can improve performance on others. And while previous attempts to create generalized robotic control policies have faced various issues, cross-former finally overcomes these limitations by directly processing diverse input data, including images from cameras in various positions and joint-position data from different robot types. As a result, in tests, cross-former demonstrated next-generation versatility. It successfully controlled single robotic arms, pairs of arms, quadrupeds, and wheeled robots, performing tasks ranging from object manipulation to navigation, with the model’s performance matching that of specialized control policies designed for each specific robot type.

Plus, cross-former even shockingly gained the ability to control a small quadcopter drone, which is an embodiment not included in its original training data, all while outperforming previous methods in this task. In fact, this kind of a generalized control policy could greatly accelerate the development and deployment of new robot designs by eliminating the need to create specialized control software for each new configuration, enhancing the overall adaptability of robots to allow them to more easily transfer skills between different tasks and environments. However, the researchers acknowledge that there are still challenges to overcome, as the current version of cross-former is computationally intensive, requiring server-based processing rather than running on the robot’s embedded chips.

And while the model can operate in real time, there are concerns about scalability as the complexity of tasks and robot designs increases. Furthermore, the development of cross-former also raises interesting questions about the nature of embodied intelligence and the potential for creating more flexible, adaptable AI systems. By demonstrating that a single model can effectively control diverse robot types, the research suggests that there may be underlying principles of control and interaction that span different physical configurations. And as research in this area progresses, we may see even further leaps in generalized robotic control policies, with future work potentially focusing on improving the efficiency of these models, enabling them to run on embedded systems and expanding their capabilities to handle even more diverse robot designs and tasks.

In fact, when the cross-former research is presented at the Conference on Robot Learning later this year, it’s likely to generate significant interest from the robotics and AI communities thanks to its pivotal role in shaping the future of robotics, leading to more versatile, adaptable and capable robotic systems across a wide range of applications. Most importantly, though, this research also highlights the growing synergy between different branches of AI research, with the success of the transformer architecture tackling complex robotics problems to demonstrate the potential for cross-pollination of ideas between different AI domains. And as the robot tech explosion gets closer, we’ll likely see increasingly sophisticated AI systems that are capable of generalizing across multiple domains.

But video is also having its breakthrough moment, as Meta just introduced MovieGen, its newest and most advanced suite of AI models for digital content creation, including four next-gen capabilities, and what MovieGen can do as a result is truly amazing. The first of these capabilities is MovieGen’s Video Generation component, which utilizes a 30B parameter transformer model optimized for both text-to-image and text-to-video tasks. It can produce high-definition videos up to 16 seconds long at 16 frames per second. The model demonstrates proficiency in representing object motion, subject-object interactions and camera movements. It can generate plausible motions for a wide range of concepts, positioning it as a competitive option in its category.

Second, for personalized video generation, MovieGen expands on the foundation model to create videos featuring specific individuals. The system takes a person’s image and a text prompt as input, generating a video that incorporates the reference person while adhering to the details specified in the prompt. Third is MovieGen’s precise video editing capability that allows for both localized and global modifications to existing videos. It can add, remove, or replace elements, as well as alter backgrounds or styles. This editing process is designed to maintain the integrity of the original content while only modifying the relevant pixels as specified by the text prompt.

And fourth is Audio Generation, for which MovieGen employs a 13B parameter model capable of creating high-fidelity audio for videos up to 45 seconds in length. This includes ambient sound, sound effects, and even instrumental background music, all synchronized with the video content. Plus, an audio extension technique has been developed to generate coherent audio for videos of any length, aiming to achieve high performance in audio quality and alignment with both video and text inputs. But the development of MovieGen involved advancements in several technical areas, including model architecture, training objectives, data processing, evaluation methods, and inference optimizations. As a result, human evaluation comparisons indicate a preference for MovieGen’s output across all four capabilities when compared to other industry models, though specific metrics and comparisons are detailed in the associated research paper.

But Meta still acknowledges that the current models have limitations and areas for improvement, with future work focusing on reducing inference time and enhancing model quality through further scaling. And as MovieGen progresses towards potential public release, Meta is collaborating with filmmakers and creators to incorporate their feedback, with MovieGen potentially serving first as a social media content creation tool, and more use cases soon to follow. And finally, Meta notes that there are still many different optimizations they can employ to further minimize inference time, as well as improve the quality of the models by scaling up further.

Because of this, MovieGen will likely continue to improve, resulting in an even more useful tool once it’s released. Anyways, resistance is futile, so don’t forget to like and subscribe and check out these bonus clips. Thanks for watching! Thank you for watching!
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