# PUMA: Perception-driven Unified Foothold Prior for Mobility Augmented Quadruped Parkour

arXiv: https://arxiv.org/abs/2601.15995 · HTML: https://mlsanity.com/paper/2601.15995/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 29 references that are in the map.
- First posted on arXiv 22 Jan 2026.

## Builds on (most cited first, 22 of 22)

- [Asymmetric Actor Critic for Image-Based Robot Learning](https://mlsanity.com/paper/1710.06542/) (2017)
- [Extreme Parkour with Legged Robots](https://mlsanity.com/paper/2309.14341/) (2023)
- [ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots](https://mlsanity.com/paper/2306.14874/) (2023)
- [Legged Locomotion in Challenging Terrains using Egocentric Vision](https://mlsanity.com/paper/2211.07638/) (2022)
- [Robot Parkour Learning](https://mlsanity.com/paper/2309.05665/) (2023)
- [Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions](https://mlsanity.com/paper/2203.15103/) (2022)
- [Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers](https://mlsanity.com/paper/2107.03996/) (2021)
- [DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning](https://mlsanity.com/paper/2301.10602/) (2023)
- [DTC: Deep Tracking Control](https://mlsanity.com/paper/2309.15462/) (2023)
- [Coupling Vision and Proprioception for Navigation of Legged Robots](https://mlsanity.com/paper/2112.02094/) (2021)
- [Learning Humanoid Standing-up Control across Diverse Postures](https://mlsanity.com/paper/2502.08378/) (2025)
- [Attention-Based Map Encoding for Learning Generalized Legged Locomotion](https://mlsanity.com/paper/2506.09588/) (2025)
- [PIE: Parkour with Implicit-Explicit Learning Framework for Legged Robots](https://mlsanity.com/paper/2408.13740/) (2024)
- [High-speed control and navigation for quadrupedal robots on complex and discrete terrain](https://mlsanity.com/paper/2506.02835/) (2025)
- [RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards](https://mlsanity.com/paper/2407.11562/) (2024)
- [Continuous Jumping for Legged Robots on Stepping Stones via Trajectory Optimization and Model Predictive Control](https://mlsanity.com/paper/2204.01147/) (2022)
- [Spatio-Temporal Motion Retargeting for Quadruped Robots](https://mlsanity.com/paper/2404.11557/) (2024)
- [SARO: Space-Aware Robot System for Terrain Crossing via Vision-Language Model](https://mlsanity.com/paper/2407.16412/) (2024)
- [DFM: Deep Fourier Mimic for Expressive Dance Motion Learning](https://mlsanity.com/paper/2502.10980/) (2025)
- [Multi-critic Learning for Whole-body End-effector Twist Tracking](https://mlsanity.com/paper/2507.08656/) (2025)
- [START: Traversing Sparse Footholds with Terrain Reconstruction](https://mlsanity.com/paper/2512.13153/) (2025)
- [Hierarchical Reinforcement Learning and Value Optimization for Challenging Quadruped Locomotion](https://mlsanity.com/paper/2506.20036/) (2025)

## Built on by (most cited first, 1 of 1)

- [GUIDE: Goal-Initialized Directional Understanding for End-to-End Legged Navigation](https://mlsanity.com/paper/2606.10832/) (2026)

## Abstract (arXiv)

Parkour tasks for quadrupeds have emerged as a promising benchmark for agile locomotion. While human athletes can effectively perceive environmental characteristics to select appropriate footholds for obstacle traversal, endowing legged robots with similar perceptual reasoning remains a significant challenge. Existing methods often rely on hierarchical controllers that follow pre-computed footholds, thereby constraining the robot's real-time adaptability and the exploratory potential of reinforcement learning. To overcome these challenges, we present PUMA, an end-to-end learning framework that integrates visual perception and foothold priors into a single-stage training process. This approach leverages terrain features to estimate egocentric polar foothold priors, composed of relative distance and heading, guiding the robot in active posture adaptation for parkour tasks. Extensive experiments conducted in simulation and real-world environments across various discrete complex terrains, demonstrate PUMA's exceptional agility and robustness in challenging scenarios.