  Embodied instruction following is a challenging problem requiring an agent to
infer a sequence of primitive actions to achieve a goal environment state from
complex language and visual inputs. Action Learning From Realistic Environments
and Directives (ALFRED) is a recently proposed benchmark for this problem
consisting of step-by-step natural language instructions to achieve subgoals
which compose to an ultimate high-level goal. Key challenges for this task
include localizing target locations and navigating to them through visual
inputs, and grounding language instructions to visual appearance of objects. To
address these challenges, in this study, we augment the agent's field of view
during navigation subgoals with multiple viewing angles, and train the agent to
predict its relative spatial relation to the target location at each timestep.
We also improve language grounding by introducing a pre-trained object
detection module to the model pipeline. Empirical studies show that our
approach exceeds the baseline model performance.
