  Decision making via sequence modeling aims to mimic the success of language
models, where actions taken by an embodied agent are modeled as tokens to
predict. Despite their promising performance, it remains unclear if embodied
sequence modeling leads to the emergence of internal representations that
represent the environmental state information. A model that lacks abstract
state representations would be liable to make decisions based on surface
statistics which fail to generalize. We take the BabyAI environment, a grid
world in which language-conditioned navigation tasks are performed, and build a
sequence modeling Transformer, which takes a language instruction, a sequence
of actions, and environmental observations as its inputs. In order to
investigate the emergence of abstract state representations, we design a
"blindfolded" navigation task, where only the initial environmental layout, the
language instruction, and the action sequence to complete the task are
available for training. Our probing results show that intermediate
environmental layouts can be reasonably reconstructed from the internal
activations of a trained model, and that language instructions play a role in
the reconstruction accuracy. Our results suggest that many key features of
state representations can emerge via embodied sequence modeling, supporting an
optimistic outlook for applications of sequence modeling objectives to more
complex embodied decision-making domains.
