# Relevance-guided Unsupervised Discovery of Abilities with Quality-Diversity Algorithms

arXiv: https://arxiv.org/abs/2204.09828 · HTML: https://mlsanity.com/paper/2204.09828/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 43 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [Robots that can adapt like animals](https://mlsanity.com/paper/1407.3501/) (2014)
- [Quality and Diversity Optimization: A Unifying Modular Framework](https://mlsanity.com/paper/1708.09251/) (2017)
- [Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space](https://mlsanity.com/paper/1912.02400/) (2019)
- [Differentiable Quality Diversity](https://mlsanity.com/paper/2106.03894/) (2021)
- [Quality-Diversity Optimization: a novel branch of stochastic optimization](https://mlsanity.com/paper/2012.04322/) (2020)
- [Autonomous skill discovery with Quality-Diversity and Unsupervised Descriptors](https://mlsanity.com/paper/1905.11874/) (2019)
- [Unsupervised Behaviour Discovery with Quality-Diversity Optimisation](https://mlsanity.com/paper/2106.05648/) (2021)
- [Reset-free Trial-and-Error Learning for Robot Damage Recovery](https://mlsanity.com/paper/1610.04213/) (2016)
- [Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration](https://mlsanity.com/paper/1803.00781/) (2018)
- [Unsupervised Learning and Exploration of Reachable Outcome Space](https://mlsanity.com/paper/1909.05508/) (2019)
- [Quality Diversity for Multi-task Optimization](https://mlsanity.com/paper/2003.04407/) (2020)
- [Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites](https://mlsanity.com/paper/1906.05175/) (2019)
- [Deep Learning a Grasp Function for Grasping under Gripper Pose Uncertainty](https://mlsanity.com/paper/1608.02239/) (2016)
- [From exploration to control: learning object manipulation skills through novelty search and local adaptation](https://mlsanity.com/paper/1901.00811/) (2019)
- [Sparse Reward Exploration via Novelty Search and Emitters](https://mlsanity.com/paper/2102.03140/) (2021)
- [Transforming Exploratory Creativity with DeLeNoX](https://mlsanity.com/paper/2103.11715/) (2021)
- [Adaptive Prior Selection for Repertoire-based Online Adaptation in Robotics](https://mlsanity.com/paper/1907.07029/) (2019)
- [Interactive Constrained MAP-Elites: Analysis and Evaluation of the Expressiveness of the Feature Dimensions](https://mlsanity.com/paper/2003.03377/) (2020)
- [BR-NS: an Archive-less Approach to Novelty Search](https://mlsanity.com/paper/2104.03936/) (2021)
- [Discovering and Exploiting Sparse Rewards in a Learned Behavior Space](https://mlsanity.com/paper/2111.01919/) (2021)
- [Ensemble Feature Extraction for Multi-Container Quality-Diversity Algorithms](https://mlsanity.com/paper/2105.00682/) (2021)

## Built on by (most cited first, 8 of 8)

- [Covariance Matrix Adaptation MAP-Annealing](https://mlsanity.com/paper/2205.10752/) (2022)
- [Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery](https://mlsanity.com/paper/2210.03516/) (2022)
- [Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics](https://mlsanity.com/paper/2403.09930/) (2024)
- [Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization](https://mlsanity.com/paper/2310.12103/) (2023)
- [Diversity from Human Feedback](https://mlsanity.com/paper/2310.06648/) (2023)
- [Multi-Objective Quality-Diversity in Unstructured and Unbounded Spaces](https://mlsanity.com/paper/2504.03715/) (2025)
- [Discount Model Search for Quality Diversity Optimization in High-Dimensional Measure Spaces](https://mlsanity.com/paper/2601.01082/) (2026)
- [From Tabula Rasa to Emergent Abilities: Discovering Robot Skills via Real-World Unsupervised Quality-Diversity](https://mlsanity.com/paper/2508.19172/) (2025)

## Abstract (arXiv)

Quality-Diversity algorithms provide efficient mechanisms to generate large collections of diverse and high-performing solutions, which have shown to be instrumental for solving downstream tasks. However, most of those algorithms rely on a behavioural descriptor to characterise the diversity that is hand-coded, hence requiring prior knowledge about the considered tasks. In this work, we introduce Relevance-guided Unsupervised Discovery of Abilities; a Quality-Diversity algorithm that autonomously finds a behavioural characterisation tailored to the task at hand. In particular, our method introduces a custom diversity metric that leads to higher densities of solutions near the areas of interest in the learnt behavioural descriptor space. We evaluate our approach on a simulated robotic environment, where the robot has to autonomously discover its abilities based on its full sensory data. We evaluated the algorithms on three tasks: navigation to random targets, moving forward with a high velocity, and performing half-rolls. The experimental results show that our method manages to discover collections of solutions that are not only diverse, but also well-adapted to the considered downstream task.