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. A promising
PaLM-E- An Embodied Multimodal Language Model