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.

本研究旨在使机器人能够学习如何按照自然语言指示序列化其动作以执行任务，通过人类伙伴的成功演示。为了达到这个目的，我们引入了一种新颖的神经符号模型 GoalNet，它能够从人类演示和语言任务描述中推断目标谓词的上下文和任务依赖关系，并结合学习和规划以提高在多阶段任务中的决策能力。通过在一个表示语言变化的基准数据集上进行测试，我们证明了 GoalNet 在任务完成率上比现有基于规则的方法有了显著改进（51％）。