Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning
This work addresses the problem of enabling robots to perform complex, extended tasks more effectively for robotics applications, representing an incremental advancement by combining neuro-symbolic methods with imitation learning.
The paper tackles the challenge of teaching robots long, multi-step tasks through imitation learning by proposing a neuro-symbolic framework that learns symbolic abstractions from demonstrations to decompose tasks and plan sequences, resulting in increased data efficiency, improved generalization, and enhanced interpretability in simulated robotic environments.
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.