# From Tabula Rasa to Emergent Abilities: Discovering Robot Skills via Real-World Unsupervised Quality-Diversity

arXiv: https://arxiv.org/abs/2508.19172 · HTML: https://mlsanity.com/paper/2508.19172/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 34 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Builds on (most cited first, 23 of 23)

- [Diversity is All You Need: Learning Skills without a Reward Function](https://mlsanity.com/paper/1802.06070/) (2018)
- [DayDreamer: World Models for Physical Robot Learning](https://mlsanity.com/paper/2206.14176/) (2022)
- [Dynamics-Aware Unsupervised Discovery of Skills](https://mlsanity.com/paper/1907.01657/) (2019)
- [Robots that can adapt like animals](https://mlsanity.com/paper/1407.3501/) (2014)
- [SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning](https://mlsanity.com/paper/2401.16013/) (2024)
- [Quality and Diversity Optimization: A Unifying Modular Framework](https://mlsanity.com/paper/1708.09251/) (2017)
- [Learning to Walk in the Real World with Minimal Human Effort](https://mlsanity.com/paper/2002.08550/) (2020)
- [METRA: Scalable Unsupervised RL with Metric-Aware Abstraction](https://mlsanity.com/paper/2310.08887/) (2023)
- [One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL](https://mlsanity.com/paper/2010.14484/) (2020)
- [Autonomous skill discovery with Quality-Diversity and Unsupervised Descriptors](https://mlsanity.com/paper/1905.11874/) (2019)
- [Controllability-Aware Unsupervised Skill Discovery](https://mlsanity.com/paper/2302.05103/) (2023)
- [Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality](https://mlsanity.com/paper/2205.13521/) (2022)
- [Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning](https://mlsanity.com/paper/2004.12974/) (2020)
- [Reset-free Trial-and-Error Learning for Robot Damage Recovery](https://mlsanity.com/paper/1610.04213/) (2016)
- [Unsupervised Learning and Exploration of Reachable Outcome Space](https://mlsanity.com/paper/1909.05508/) (2019)
- [Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics](https://mlsanity.com/paper/2403.09930/) (2024)
- [Adaptive Prior Selection for Repertoire-based Online Adaptation in Robotics](https://mlsanity.com/paper/1907.07029/) (2019)
- [Relevance-guided Unsupervised Discovery of Abilities with Quality-Diversity Algorithms](https://mlsanity.com/paper/2204.09828/) (2022)
- [Learning Diverse Skills for Local Navigation under Multi-constraint Optimality](https://mlsanity.com/paper/2310.02440/) (2023)
- [Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion](https://mlsanity.com/paper/2503.08375/) (2025)
- [Learning to Walk Autonomously via Reset-Free Quality-Diversity](https://mlsanity.com/paper/2204.03655/) (2022)
- [Discovering and Exploiting Sparse Rewards in a Learned Behavior Space](https://mlsanity.com/paper/2111.01919/) (2021)
- [Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning](https://mlsanity.com/paper/2304.12080/) (2023)

## Abstract (arXiv)

Autonomous skill discovery aims to enable robots to acquire diverse behaviors without explicit supervision. Learning such behaviors directly on physical hardware remains challenging due to safety and data efficiency constraints. Existing methods, including Quality-Diversity Actor-Critic (QDAC), require manually defined skill spaces and carefully tuned heuristics, limiting real-world applicability. We propose Unsupervised Real-world Skill Acquisition (URSA), an extension of QDAC that enables robots to autonomously discover and master diverse, high-performing skills directly in the real world. We demonstrate that URSA successfully discovers diverse locomotion skills on a Unitree A1 quadruped in both simulation and the real world. Our approach supports both heuristic-driven skill discovery and fully unsupervised settings. We also show that the learned skill repertoire can be reused for downstream tasks such as real-world damage adaptation, where URSA outperforms all baselines in 5 out of 9 simulated and 3 out of 5 real-world damage scenarios. Our results establish a new framework for real-world robot learning that enables continuous skill discovery with limited human intervention, representing a significant step toward more autonomous and adaptable robotic systems. Demonstration videos are available at https://adaptive-intelligent-robotics.github.io/URSA.