# Discovering Unsupervised Behaviours from Full-State Trajectories

arXiv: https://arxiv.org/abs/2211.15451 · HTML: https://mlsanity.com/paper/2211.15451/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 30 references that are in the map.
- First posted on arXiv 22 Nov 2022.

## Builds on (most cited first, 20 of 20)

- [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)
- [Diversity is All You Need: Learning Skills without a Reward Function](https://mlsanity.com/paper/1802.06070/) (2018)
- [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)
- [Dynamics-Aware Unsupervised Discovery of Skills](https://mlsanity.com/paper/1907.01657/) (2019)
- [Variational Intrinsic Control](https://mlsanity.com/paper/1611.07507/) (2016)
- [Robots that can adapt like animals](https://mlsanity.com/paper/1407.3501/) (2014)
- [Behavior From the Void: Unsupervised Active Pre-Training](https://mlsanity.com/paper/2103.04551/) (2021)
- [Quality and Diversity Optimization: A Unifying Modular Framework](https://mlsanity.com/paper/1708.09251/) (2017)
- [APS: Active Pretraining with Successor Features](https://mlsanity.com/paper/2108.13956/) (2021)
- [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 and Exploration of Reachable Outcome Space](https://mlsanity.com/paper/1909.05508/) (2019)
- [Deep Learning a Grasp Function for Grasping under Gripper Pose Uncertainty](https://mlsanity.com/paper/1608.02239/) (2016)
- [Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems](https://mlsanity.com/paper/2007.01195/) (2020)
- [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)

## Built on by (most cited first, 2 of 2)

- [AutoQD: Automatic Discovery of Diverse Behaviors with Quality-Diversity Optimization](https://mlsanity.com/paper/2506.05634/) (2025)
- [Guiding Evolution of Artificial Life Using Vision-Language Models](https://mlsanity.com/paper/2509.22447/) (2025)

## Abstract (arXiv)

Improving open-ended learning capabilities is a promising approach to enable robots to face the unbounded complexity of the real-world. Among existing methods, the ability of Quality-Diversity algorithms to generate large collections of diverse and high-performing skills is instrumental in this context. However, most of those algorithms rely on a hand-coded behavioural descriptor to characterise the diversity, hence requiring prior knowledge about the considered tasks. In this work, we propose an additional analysis of Autonomous Robots Realising their Abilities; a Quality-Diversity algorithm that autonomously finds behavioural characterisations. We evaluate this approach on a simulated robotic environment, where the robot has to autonomously discover its abilities from its full-state trajectories. All algorithms were applied to three tasks: navigation, moving forward with a high velocity, and performing half-rolls. The experimental results show that the algorithm under study discovers autonomously collections of solutions that are diverse with respect to all tasks. More specifically, the analysed approach autonomously finds policies that make the robot move to diverse positions, but also utilise its legs in diverse ways, and even perform half-rolls.