In robotic domains, learning and planning are complicated by continuous state
spaces, continuous action spaces, and long task horizons. In this work, we
address these challenges with Neuro-Symbolic Relational Transition Models
(NSRTs), a novel class of models that are data-efficient to learn, compatible
with powerful robotic planning methods, and generalizable over objects. NSRTs
have both symbolic and neural components, enabling a bilevel planning scheme
where symbolic AI planning in an outer loop guides continuous planning with
neural models in an inner loop. Experiments in four robotic planning domains
show that NSRTs can be learned after only tens or hundreds of training
episodes, and then used for fast planning in new tasks that require up to 60
actions and involve many more objects than were seen during training. Video:
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本研究提出了一种新型模型 NSRTs，它具有符号和神经组件，在很少的训练次数后就可以用于快速规划，并可以泛化到更多的物体和行为，具有数据高效的特点。