Show HN: We trained a (mini) GPT for multi-modal robot behaviors
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First author of the paper here, thought some of you may enjoy reading about this! Even now, training robots on human demonstration data is the best way to get them to do new and exciting things in the real world. However, this generally requires a lot of data curation in the standard way: the robots can only follow along if you give them data that is solving a single task in a single way.
To improve the status quo, we introduce Behavior Transformer in this paper, which can learn from unlabeled demonstration data solving multiple different tasks in different ways using a GPT-like generator model. We had to make some modifications to fit the continuous actions, unlike the standard GPT model which fits discrete words.
As it turns out, unconditional rollouts from this model shows a lot more "natural" behavior (i.e. different tasks solved in different rollouts in different ways)_than standard behavioral cloning. More importantly, behavior transformers show much better mode coverage compared to previous models, and show some level of compositionality. Check out our videos! [1]
Finally, another oft-ignored part I am quite proud of is our code release -- we worked quite hard to make sure our code [2] is easy to read, reproduce, and remix! And also, did I tell you that these models train super fast? The Franka Kitchen environment in the top video [3] takes just 10 minutes on an Nvidia 3080 to the point you are seeing in the video. Compare that with standard RL training, and you might agree with me that a small number of demonstrations can truly go a long way!
Happy to answer questions, as well! Have a great Friday, wherever you are :)