# Divide, Discover, Deploy: Factorized Skill Learning with Symmetry and Style Priors

arXiv: https://arxiv.org/abs/2508.19953 · HTML: https://mlsanity.com/paper/2508.19953/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2025.

## Built on by (most cited first, 4 of 4)

- [Reference Grounded Skill Discovery](https://mlsanity.com/paper/2510.06203/) (2025)
- [InSight: Self-Guided Skill Acquisition via Steerable VLAs](https://mlsanity.com/paper/2606.24884/) (2026)
- [Periodic Skill Discovery](https://mlsanity.com/paper/2511.03187/) (2025)
- [DSD: Learning Diverse and Reusable Motor Skills via Diffusion Skill Discovery](https://mlsanity.com/paper/2609.17682/) (2026)

## Abstract (arXiv)

Unsupervised Skill Discovery (USD) allows agents to autonomously learn diverse behaviors without task-specific rewards. While recent USD methods have shown promise, their application to real-world robotics remains underexplored. In this paper, we propose a modular USD framework to address the challenges in the safety, interpretability, and deployability of the learned skills. Our approach employs user-defined factorization of the state space to learn disentangled skill representations. It assigns different skill discovery algorithms to each factor based on the desired intrinsic reward function. To encourage structured morphology-aware skills, we introduce symmetry-based inductive biases tailored to individual factors. We also incorporate a style factor and regularization penalties to promote safe and robust behaviors. We evaluate our framework in simulation using a quadrupedal robot and demonstrate zero-shot transfer of the learned skills to real hardware. Our results show that factorization and symmetry lead to the discovery of structured human-interpretable behaviors, while the style factor and penalties enhance safety and diversity. Additionally, we show that the learned skills can be used for downstream tasks and perform on par with oracle policies trained with hand-crafted rewards.