  Our goal is to enable a robot to learn how to sequence its actions to perform
tasks specified as natural language instructions, given successful
demonstrations from a human partner. The ability to plan high-level tasks can
be factored as (i) inferring specific goal predicates that characterize the
task implied by a language instruction for a given world state and (ii)
synthesizing a feasible goal-reaching action-sequence with such predicates. For
the former, we leverage a neural network prediction model, while utilizing a
symbolic planner for the latter. We introduce a novel neuro-symbolic model,
GoalNet, for contextual and task dependent inference of goal predicates from
human demonstrations and linguistic task descriptions. GoalNet combines (i)
learning, where dense representations are acquired for language instruction and
the world state that enables generalization to novel settings and (ii)
planning, where the cause-effect modeling by the symbolic planner eschews
irrelevant predicates facilitating multi-stage decision making in large
domains. GoalNet demonstrates a significant improvement (51%) in the task
completion rate in comparison to a state-of-the-art rule-based approach on a
benchmark data set displaying linguistic variations, particularly for
multi-stage instructions.
