Ted Xiao

TL;DR: It is shown how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment.

TL;DR: In this article , the authors investigate the extent to which large language models can reason over sources of feedback provided through natural language, without any additional training, such as success detection, scene description, and human interaction.

TL;DR: Play-LMP is introduced, a method designed to handle variability in the LfP setting by organizing it in an embedding space and finding that play-supervised models, unlike their expert-trained counterparts, are more robust to perturbations and exhibit retrying-till-success.

TL;DR: In this article, a self-supervised method that learns to organize play behaviors in a latent space, then reuse them at test time to achieve specific goals is proposed. But this method requires a large amount of play data.

TL;DR: In this article , the authors present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties and verify their conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size and data diversity based on a large-scale data collection on real robots performing real-world tasks.