# PIE: Parkour with Implicit-Explicit Learning Framework for Legged Robots

arXiv: https://arxiv.org/abs/2408.13740 · HTML: https://mlsanity.com/paper/2408.13740/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2024.

## Built on by (most cited first, 25 of 25)

- [ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills](https://mlsanity.com/paper/2502.01143/) (2025)
- [MoE-Loco: Mixture of Experts for Multitask Locomotion](https://mlsanity.com/paper/2503.08564/) (2025)
- [LocoFormer: Generalist Locomotion via Long-context Adaptation](https://mlsanity.com/paper/2509.23745/) (2025)
- [Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching](https://mlsanity.com/paper/2602.15827/) (2026)
- [World Model-based Perception for Visual Legged Locomotion](https://mlsanity.com/paper/2409.16784/) (2024)
- [MoRE: Mixture of Residual Experts for Humanoid Lifelike Gaits Learning on Complex Terrains](https://mlsanity.com/paper/2506.08840/) (2025)
- [Robust Robot Walker: Learning Agile Locomotion over Tiny Traps](https://mlsanity.com/paper/2409.07409/) (2024)
- [Unsupervised Skill Discovery as Exploration for Learning Agile Locomotion](https://mlsanity.com/paper/2508.08982/) (2025)
- [Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots](https://mlsanity.com/paper/2507.20217/) (2025)
- [Gait-Adaptive Perceptive Humanoid Locomotion with Real-Time Under-Base Terrain Reconstruction](https://mlsanity.com/paper/2512.07464/) (2025)
- [MARG: MAstering Risky Gap Terrains for Legged Robots with Elevation Mapping](https://mlsanity.com/paper/2509.20036/) (2025)
- [DeFM: Learning Foundation Representations from Depth for Robotics](https://mlsanity.com/paper/2601.18923/) (2026)
- [Reinforcement Learning from Wild Animal Videos](https://mlsanity.com/paper/2412.04273/) (2024)
- [LIPM-Guided Reinforcement Learning for Stable and Perceptive Locomotion in Bipedal Robots](https://mlsanity.com/paper/2509.09106/) (2025)
- [VMTS: Vision-Assisted Teacher-Student Reinforcement Learning for Multi-Terrain Locomotion in Bipedal Robots](https://mlsanity.com/paper/2503.07049/) (2025)
- [Flexible Locomotion Learning with Diffusion Model Predictive Control](https://mlsanity.com/paper/2510.04234/) (2025)
- [PUMA: Perception-driven Unified Foothold Prior for Mobility Augmented Quadruped Parkour](https://mlsanity.com/paper/2601.15995/) (2026)
- [Global-Local Attention Decomposition for Terrain Encoding in Humanoid Perceptive Locomotion](https://mlsanity.com/paper/2606.00637/) (2026)
- [Agile perceptive multi-skill locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2607.13579/) (2026)
- [Trajectory-Level Automatic Curriculum Learning for Legged Locomotion on Unstructured Terrain](https://mlsanity.com/paper/2608.16164/) (2026)
- [PPL: Point Cloud Supervised Proprioceptive Locomotion Reinforcement Learning for Legged Robots in Crawl Spaces](https://mlsanity.com/paper/2508.09950/) (2025)
- [Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2602.10514/) (2026)
- [CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation](https://mlsanity.com/paper/2606.04718/) (2026)
- [Long-Distance Real-World Navigation of the Legged-Wheeled Robot Go2-W Using Deep Reinforcement Learning](https://mlsanity.com/paper/2606.21387/) (2026)
- [The Neverwhere Visual Parkour Benchmark Suite](https://mlsanity.com/paper/2609.16443/) (2026)

## Abstract (arXiv)

Parkour presents a highly challenging task for legged robots, requiring them to traverse various terrains with agile and smooth locomotion. This necessitates comprehensive understanding of both the robot's own state and the surrounding terrain, despite the inherent unreliability of robot perception and actuation. Current state-of-the-art methods either rely on complex pre-trained high-level terrain reconstruction modules or limit the maximum potential of robot parkour to avoid failure due to inaccurate perception. In this paper, we propose a one-stage end-to-end learning-based parkour framework: Parkour with Implicit-Explicit learning framework for legged robots (PIE) that leverages dual-level implicit-explicit estimation. With this mechanism, even a low-cost quadruped robot equipped with an unreliable egocentric depth camera can achieve exceptional performance on challenging parkour terrains using a relatively simple training process and reward function. While the training process is conducted entirely in simulation, our real-world validation demonstrates successful zero-shot deployment of our framework, showcasing superior parkour performance on harsh terrains.