用符号抽象提升机器人长任务学习的数据效率与可解释性
Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning
- 结合神经网络与符号推理，从示范中自动提取任务抽象结构
- 在三个仿真环境上实现更高数据效率与泛化能力
- 适合需要可解释、可复用技能的机器人系统开发
模仿学习是教授机器人新行为的常用方法，但现有方法多聚焦于短时、孤立技能，难以应对长序列多步骤任务。为此，本文提出一种神经符号模仿学习框架：通过任务示范，系统首先学习低维状态-动作空间的符号化表示，将任务分解为更易处理的子任务，并利用符号规划生成高层抽象计划；随后，基于该分解结构，训练一组神经技能，将抽象计划转化为具体机器人指令。在三个仿真机器人环境中的实验表明，相比基线方法，本方法显著提升数据效率、泛化能力，并增强决策可解释性。
原文摘要 · Abstract (English)
Imitation learning is a popular method for teaching robots new behaviors. However, most existing methods focus on teaching short, isolated skills rather than long, multi-step tasks. To bridge this gap, imitation learning algorithms must not only learn individual skills but also an abstract understanding of how to sequence these skills to perform extended tasks effectively. This paper addresses this challenge by proposing a neuro-symbolic imitation learning framework. Using task demonstrations, the system first learns a symbolic representation that abstracts the low-level state-action space. The learned representation decomposes a task into easier subtasks and allows the system to leverage symbolic planning to generate abstract plans. Subsequently, the system utilizes this task decomposition to learn a set of neural skills capable of refining abstract plans into actionable robot commands. Experimental results in three simulated robotic environments demonstrate that, compared to baselines, our neuro-symbolic approach increases data efficiency, improves generalization capabilities, and facilitates interpretability.
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