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The Robot Report
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MIT develops multimodal technique to train robots
- MIT researchers have developed a technique that combines data from many sources to teach any robot a vast range of tasks.
- This method requires fewer task-specific data and combines simulations and real-world data.
- This approach can be used to train robots quickly without the need to start training a robot from scratch.
- Their new technique aligns data from varied domains and multiple modalities into a shared 'language' that generative AI models process.
- Their architecture is called Heterogeneous Pretrained Transformers that unifies data from varied modalities and domains.
- Proprioception data is key to enable dexterous motions, and it is placed with the same importance as vision data in the architecture.
- HPT improved robot performance by more than 20% on simulation and real-world tasks compared with a robot trained from scratch.
- In the future, the researchers want to study how data diversity could boost the performance of HPT.
- They also want to enhance the HPT so it can process unlabeled data like GPT-4 and other large language models.
- Their dream is to have a universal robot brain, which anyone can use for their robot without any training.
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