# Robust Quadrupedal Locomotion via Risk-Averse Policy Learning

arXiv: https://arxiv.org/abs/2308.09405 · HTML: https://mlsanity.com/paper/2308.09405/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2023.

## Built on by (most cited first, 19 of 19)

- [COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models](https://mlsanity.com/paper/2409.15146/) (2024)
- [Contrastive Representation for Data Filtering in Cross-Domain Offline Reinforcement Learning](https://mlsanity.com/paper/2405.06192/) (2024)
- [SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation](https://mlsanity.com/paper/2405.19586/) (2024)
- [Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers](https://mlsanity.com/paper/2405.12424/) (2024)
- [Risk-Sensitive RL for Alleviating Exploration Dilemmas in Large Language Models](https://mlsanity.com/paper/2509.24261/) (2025)
- [Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning](https://mlsanity.com/paper/2502.17219/) (2025)
- [FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots](https://mlsanity.com/paper/2505.06883/) (2025)
- [Constrained Ensemble Exploration for Unsupervised Skill Discovery](https://mlsanity.com/paper/2405.16030/) (2024)
- [Pessimistic Value Iteration for Multi-Task Data Sharing in Offline Reinforcement Learning](https://mlsanity.com/paper/2404.19346/) (2024)
- [Dual-Robust Cross-Domain Offline Reinforcement Learning Against Dynamics Shifts](https://mlsanity.com/paper/2512.02486/) (2025)
- [Bipedalism for Quadrupedal Robots: Versatile Loco-Manipulation through Risk-Adaptive Reinforcement Learning](https://mlsanity.com/paper/2507.20382/) (2025)
- [HAFO: A Force-Adaptive Control Framework for Humanoid Robots in Intense Interaction Environments](https://mlsanity.com/paper/2511.20275/) (2025)
- [Perturbation-mitigated USV Navigation with Distributionally Robust Reinforcement Learning](https://mlsanity.com/paper/2512.00030/) (2025)
- [Trajectory Entropy Reinforcement Learning for Robust Robot Motor Skill Learning](https://mlsanity.com/paper/2505.04193/) (2025)
- [Risk-Aware Reinforcement Learning with Bandit-Based Adaptation for Quadrupedal Locomotion](https://mlsanity.com/paper/2510.14338/) (2025)
- [Distributional Active Inference](https://mlsanity.com/paper/2601.20985/) (2026)
- [Risk-Aware Reinforcement Learning for Mobile Manipulation](https://mlsanity.com/paper/2603.04579/) (2026)
- [Quantile Geometry Regularization for Distributional Reinforcement Learning](https://mlsanity.com/paper/2605.08182/) (2026)
- [Predictive Safety Curricula for Robust Legged Locomotion](https://mlsanity.com/paper/2609.37070/) (2026)

## Abstract (arXiv)

The robustness of legged locomotion is crucial for quadrupedal robots in challenging terrains. Recently, Reinforcement Learning (RL) has shown promising results in legged locomotion and various methods try to integrate privileged distillation, scene modeling, and external sensors to improve the generalization and robustness of locomotion policies. However, these methods are hard to handle uncertain scenarios such as abrupt terrain changes or unexpected external forces. In this paper, we consider a novel risk-sensitive perspective to enhance the robustness of legged locomotion. Specifically, we employ a distributional value function learned by quantile regression to model the aleatoric uncertainty of environments, and perform risk-averse policy learning by optimizing the worst-case scenarios via a risk distortion measure. Extensive experiments in both simulation environments and a real Aliengo robot demonstrate that our method is efficient in handling various external disturbances, and the resulting policy exhibits improved robustness in harsh and uncertain situations in legged locomotion. Videos are available at https://risk-averse-locomotion.github.io/.