# Learning Dynamic Bipedal Walking Across Stepping Stones

arXiv: https://arxiv.org/abs/2205.01807 · HTML: https://mlsanity.com/paper/2205.01807/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2022.

## Built on by (most cited first, 9 of 9)

- [Eurekaverse: Environment Curriculum Generation via Large Language Models](https://mlsanity.com/paper/2411.01775/) (2024)
- [Integrating Model-Based Footstep Planning with Model-Free Reinforcement Learning for Dynamic Legged Locomotion](https://mlsanity.com/paper/2408.02662/) (2024)
- [Deep Reinforcement Learning for Bipedal Locomotion: A Brief Survey](https://mlsanity.com/paper/2404.17070/) (2024)
- [Gait-Conditioned Reinforcement Learning with Multi-Phase Curriculum for Humanoid Locomotion](https://mlsanity.com/paper/2505.20619/) (2025)
- [Enhancing Model-Based Step Adaptation for Push Recovery through Reinforcement Learning of Step Timing and Region](https://mlsanity.com/paper/2411.01000/) (2024)
- [No More Blind Spots: Learning Vision-Based Omnidirectional Bipedal Locomotion for Challenging Terrain](https://mlsanity.com/paper/2508.11929/) (2025)
- [Contrastive Representation Learning for Robust Sim-to-Real Transfer of Adaptive Humanoid Locomotion](https://mlsanity.com/paper/2509.12858/) (2025)
- [HuMam: Humanoid Motion Control via End-to-End Deep Reinforcement Learning with Mamba](https://mlsanity.com/paper/2509.18046/) (2025)
- [Robot Squid Game: Quadrupedal Locomotion for Traversing Narrow Tunnels](https://mlsanity.com/paper/2605.13665/) (2026)

## Abstract (arXiv)

In this work, we propose a learning approach for 3D dynamic bipedal walking when footsteps are constrained to stepping stones. While recent work has shown progress on this problem, real-world demonstrations have been limited to relatively simple open-loop, perception-free scenarios. Our main contribution is a more advanced learning approach that enables real-world demonstrations, using the Cassie robot, of closed-loop dynamic walking over moderately difficult stepping-stone patterns. Our approach first uses reinforcement learning (RL) in simulation to train a controller that maps footstep commands onto joint actions without any reference motion information. We then learn a model of that controller's capabilities, which enables prediction of feasible footsteps given the robot's current dynamic state. The resulting controller and model are then integrated with a real-time overhead camera system for detecting stepping stone locations. For evaluation, we develop a benchmark set of stepping stone patterns, which are used to test performance in both simulation and the real world. Overall, we demonstrate that sim-to-real learning is extremely promising for enabling dynamic locomotion over stepping stones. We also identify challenges remaining that motivate important future research directions.