  In learning an embodied agent executing daily tasks via language directives,
the literature largely assumes that the agent learns all training data at the
beginning. We argue that such a learning scenario is less realistic since a
robotic agent is supposed to learn the world continuously as it explores and
perceives it. To take a step towards a more realistic embodied agent learning
scenario, we propose two continual learning setups for embodied agents;
learning new behaviors (Behavior Incremental Learning, Behavior-IL) and new
environments (Environment Incremental Learning, Environment-IL) For the tasks,
previous 'data prior' based continual learning methods maintain logits for the
past tasks. However, the stored information is often insufficiently learned
information and requires task boundary information, which might not always be
available. Here, we propose to update them based on confidence scores without
task boundary information during training (i.e., task-free) in a moving average
fashion, named Confidence-Aware Moving Average (CAMA). In the proposed
Behavior-IL and Environment-IL setups, our simple CAMA outperforms prior state
of the art in our empirical validations by noticeable margins. The project page
including codes is https://github.com/snumprlab/cl-alfred.
