  Vision Transformers (ViT), when paired with large-scale pretraining, have
shown remarkable performance across various computer vision tasks, primarily
due to their weak inductive bias. However, while such weak inductive bias aids
in pretraining scalability, this may hinder the effective adaptation of ViTs
for visuo-motor control tasks as a result of the absence of control-centric
inductive biases. Such absent inductive biases include spatial locality and
translation equivariance bias which convolutions naturally offer. To this end,
we introduce Convolution Injector (CoIn), an add-on module that injects
convolutions which are rich in locality and equivariance biases into a
pretrained ViT for effective adaptation in visuo-motor control. We evaluate
CoIn with three distinct types of pretrained ViTs (CLIP, MVP, VC-1) across 12
varied control tasks within three separate domains (Adroit, MetaWorld, DMC),
and demonstrate that CoIn consistently enhances control task performance across
all experimented environments and models, validating the effectiveness of
providing pretrained ViTs with control-centric biases.
