论文标题
学习二重计划的神经符号技能
Learning Neuro-Symbolic Skills for Bilevel Planning
论文作者
论文摘要
在具有连续以对象的状态，连续的动作，长距离和稀疏反馈的机器人环境中，决策是具有挑战性的。诸如任务和运动计划（TAMP）之类的分层方法通过将决策分解为两个或多个抽象来解决这些挑战。在给出演示和符号谓词的环境中，先前的工作显示了如何通过手动设计的参数化策略来学习符号运算符和神经采样器。我们的主要贡献是一种与操作员和采样器结合使用的参数化策略的方法。这些组件包装成模块化的神经符号技能，并与搜索 - 然后样本tamp一起测序以解决新任务。在四个机器人域的实验中，我们表明我们的方法（具有神经符号技能的双重计划）可以解决具有不同初始状态，目标和对象不同的各种任务，表现优于六个基线和消融。视频：https：//youtu.be/pbfzp8rpugg代码：https：//tinyurl.com/skill-learning
Decision-making is challenging in robotics environments with continuous object-centric states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches, such as task and motion planning (TAMP), address these challenges by decomposing decision-making into two or more levels of abstraction. In a setting where demonstrations and symbolic predicates are given, prior work has shown how to learn symbolic operators and neural samplers for TAMP with manually designed parameterized policies. Our main contribution is a method for learning parameterized polices in combination with operators and samplers. These components are packaged into modular neuro-symbolic skills and sequenced together with search-then-sample TAMP to solve new tasks. In experiments in four robotics domains, we show that our approach -- bilevel planning with neuro-symbolic skills -- can solve a wide range of tasks with varying initial states, goals, and objects, outperforming six baselines and ablations. Video: https://youtu.be/PbFZP8rPuGg Code: https://tinyurl.com/skill-learning
