Unlike GATO, which was a bunch of models thrown into the same network but where they did not significantly learn from each other, this model exhibits significant transfer learning.

Among works that output actions, perhaps most similar is the approach proposed in Gato (Reed et al., 2022) which, like PaLM-E, is a generalist multi-embodiment agent. In contrast to Gato, we demonstrate positive transfer across different tasks where the model benefits from diverse joint training across multiple domains. [...]

Tab. 1 shows results for 3-5 objects when training on 1% of the dataset, which corresponds to only 320 examples for each of the two planning tasks. Here we see that there are significant differences between the input representations, especially for the planning tasks. First, pre-training the LLM is beneficial in the low data regime for state inputs. Second, both ViT variants (ViT+TL, ViT-4B) do not perform well in solving the planning tasks for this little data. However, if we co-train on all other robot environments as well as general vision-language datasets (ViT-4B generalist), then the performance of the ViT-4B more than doubles. This shows a significant transfer effect between different robot embodiments and tasks. Finally, using OSRT as the input representation leads to the best performance here, demonstrating the strengths of 3D-aware object representations. We also observe another instance of transfer here: when we remove the TAMP VQA data and only train on the 640 planning tasks examples, there is a (slight) drop in performance. The state-of-the art vision-language model PaLI (Chen et al., 2022) that was not trained on robot data is not able to solve the tasks. We only evaluated it on q2 (objects left/right/center on the table) and q3 (vertical object relations), since those most resemble typical VQA tasks. [...]

Generalist vs specialist models – transfer. As summarized in Fig. 3, we have shown several instances of transfer in this work, meaning that PaLM-E trained on different tasks and datasets at the same time leads to significantly increased performance relative to models trained separately on the different tasks alone. In Fig. 4, co-training on the “full mixture” achieves more than double the performance. In Tab. 9, we see significant improvements in performance if we add LLM/ViT pre-training, and training on the full mixture instead of the mobile manipulation data alone. For the Language-Table experiment in Tab. 2, we observe analogous behaviour.

Data efficiency. Compared to available massive language or vision-language datasets, robotics data is significantly less abundant. As discussed in the last paragraph, our model exhibits transfer, which aids PaLM-E to solve robotics tasks from very few training examples in the robotics domain, e.g. between 10 and 80 for Language Table or 320 for TAMP. The OSRT results show another instance of data-efficiency by using a geometric input representation.

I honestly now believe that AGI already exists. This model may not have been it, and we will debate for hundreds of years* about whether the threshold was transformers or MLPs or multimodal, and which first model was really the first, in the same way we still debate which electronic computer was truly the first. But I do believe that it is here.

We do not have human-level machine intelligence (HLMI) yet. These systems still have a lot of limitations, in particular the context window and lack of memory. They are very limited in some domains such as robotics. However, it seems unlikely to me that we are not already in the takeoff.

* (assuming the debate doesn't get abruptly stopped)