# Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning

Source: [Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning](<https://www.sota2.com/research/paper/68fa779c1e209131cea7455f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-11-05T05:01:09.456Z

Authors: Leon Keller, Daniel Tanneberg, Jan Peters

Published: 2025-03-27T11:50:29.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Predicate and Skill Learning on General Robotic Manipulation Tasks](<https://www.sota2.com/research/sota/predicate-and-skill-learning-on-general-robotic-manipulation-tasks.md>): Predicate and Skill Learning; General Robotic Manipulation Tasks; Number of Demonstrations: 200.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2503.21406>)

[PDF](<https://arxiv.org/pdf/2503.21406.pdf>)