1X Robotics Unveils World Model for Enhanced Robot Training
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Samman just tweeted that they have incredible outperformance on goal three. This is surprising because they have been working on agents for just over a year. If they have managed to outperform on this task, it could mean agents are coming earlier than expected. This statement is very profound. There might be demos late next year.
GPT 5 will come out soon. OpenAI's original trademark hinted that agents would come with GPT 6 and GPT 7. This means GPT 6 might be the first agent-based system from OpenAI. Things could speed up due to competition.
In a recent interview, Samman talked about five levels of AI. The first level is chatbots. We have just reached the second level, which is reasoners. The third level is agents, and the fourth is innovators. The fifth level allows full organization systems.
Moving from level one to two took a while, but reaching level two enables quicker progress to level three. Agentic experiences could be very impactful. The 01 model is a preview but shows great promise. It can reason step by step, ensuring high reliability across tasks. This makes it possible to develop agents.
01 is like a preview of GPT 5. It is a set of reasoning models, different from previous ones. We are at the GPT 2 stage of this era, and AI will scale up even more. Improvements will come quickly. In the coming months, the model will solve more problems. New ways to use these models will emerge, not just chat interfaces.
1X Robotics released information on their world model. A world model is a virtual simulator predicting how the world changes in response to a robot's actions. This helps robots imagine multiple future scenarios based on their actions. It is useful for training and improving robots.
The world model can simulate complex object interactions like doors, laundry, or moving boxes. This gives robots a better understanding of their environment. Training robots to perform many tasks is challenging. New models need to perform well across all tasks. Performance can degrade due to subtle changes in the environment, like lighting.
For example, a t-shirt folding model trained on this system showed performance degradation over 50 days. Changing environments make old experiments less reliable. This new approach helps maintain performance even as surroundings change.
These developments in AI and robotics show significant progress. They enable better understanding and interaction with tasks and environments. The future of AI and robotics looks promising with these advancements.