# A Fast, Autonomous, Bipedal Walking Behavior over Rapid Regions

arXiv: https://arxiv.org/abs/2207.08312 · HTML: https://mlsanity.com/paper/2207.08312/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2022.

## Built on by (most cited first, 3 of 3)

- [Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation](https://mlsanity.com/paper/2309.01952/) (2023)
- [VMTS: Vision-Assisted Teacher-Student Reinforcement Learning for Multi-Terrain Locomotion in Bipedal Robots](https://mlsanity.com/paper/2503.07049/) (2025)
- [Words2Contact: Identifying Support Contacts from Verbal Instructions Using Foundation Models](https://mlsanity.com/paper/2407.14229/) (2024)

## Abstract (arXiv)

In trying to build humanoid robots that perform useful tasks in a world built for humans, we address the problem of autonomous locomotion. Humanoid robot planning and control algorithms for walking over rough terrain are becoming increasingly capable. At the same time, commercially available depth cameras have been getting more accurate and GPU computing has become a primary tool in AI research. In this paper, we present a newly constructed behavior control system for achieving fast, autonomous, bipedal walking, without pauses or deliberation. We achieve this using a recently published rapid planar regions perception algorithm, a height map based body path planner, an A* footstep planner, and a momentum-based walking controller. We put these elements together to form a behavior control system supported by modern software development practices and simulation tools.