Generative self-supervised model for robotics data.
Highlights:
- Transformer model for autoregressive prediction of states and actions over time to implicitly encode dynamics and behaviors for a particular robot
- The representation learned can be fine-tuned to distinct tasks (navigation, mapping, localization) with minimal data
- The base transformer model serves as a generative robotics model, similarly to GPT-3, and can be prompted to result in different robot behaviors
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[ 4.3 ms ] story [ 13.0 ms ] threadHighlights: - Transformer model for autoregressive prediction of states and actions over time to implicitly encode dynamics and behaviors for a particular robot - The representation learned can be fine-tuned to distinct tasks (navigation, mapping, localization) with minimal data - The base transformer model serves as a generative robotics model, similarly to GPT-3, and can be prompted to result in different robot behaviors
Paper: https://arxiv.org/abs/2209.11133 Video: https://youtu.be/mNQvQu_atuw Code and data: https://github.com/microsoft/PACT