# Accessibility-Based Clustering for Efficient Learning of Locomotion Skills

arXiv: https://arxiv.org/abs/2109.11191 · HTML: https://mlsanity.com/paper/2109.11191/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2021.

## Built on by (most cited first, 5 of 5)

- [Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion](https://mlsanity.com/paper/2401.17583/) (2024)
- [Generating a Terrain-Robustness Benchmark for Legged Locomotion: A Prototype via Terrain Authoring and Active Learning](https://mlsanity.com/paper/2208.07681/) (2022)
- [Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots](https://mlsanity.com/paper/2506.05516/) (2025)
- [Tiny Reinforcement Learning for Quadruped Locomotion using Decision Transformers](https://mlsanity.com/paper/2402.13201/) (2024)
- [Custom Sine Waves Are Enough for Imitation Learning of Bipedal Gaits with Different Styles](https://mlsanity.com/paper/2204.04157/) (2022)

## Abstract (arXiv)

For model-free deep reinforcement learning of quadruped locomotion, the initialization of robot configurations is crucial for data efficiency and robustness. This work focuses on algorithmic improvements of data efficiency and robustness simultaneously through automatic discovery of initial states, which is achieved by our proposed K-Access algorithm based on accessibility metrics. Specifically, we formulated accessibility metrics to measure the difficulty of transitions between two arbitrary states, and proposed a novel K-Access algorithm for state-space clustering that automatically discovers the centroids of the static-pose clusters based on the accessibility metrics. By using the discovered centroidal static poses as the initial states, we can improve data efficiency by reducing redundant explorations, and enhance the robustness by more effective explorations from the centroids to sampled poses. Focusing on fall recovery as a very hard set of locomotion skills, we validated our method extensively using an 8-DoF quadrupedal robot Bittle. Compared to the baselines, the learning curve of our method converges much faster, requiring only 60% of training episodes. With our method, the robot can successfully recover to standing poses within 3 seconds in 99.4% of the test cases. Moreover, the method can generalize to other difficult skills successfully, such as backflipping.