  We introduce a novel setting, wherein an agent needs to learn a task from a
demonstration of a related task with the difference between the tasks
communicated in natural language. The proposed setting allows reusing
demonstrations from other tasks, by providing low effort language descriptions,
and can also be used to provide feedback to correct agent errors, which are
both important desiderata for building intelligent agents that assist humans in
daily tasks. To enable progress in this proposed setting, we create two
benchmarks -- Room Rearrangement and Room Navigation -- that cover a diverse
set of task adaptations. Further, we propose a framework that uses a
transformer-based model to reason about the entities in the tasks and their
relationships, to learn a policy for the target task
