Dynamic jumping on high platforms and over gaps differentiates legged robots from wheeled counterparts. Compared to walking on rough terrains, dynamic locomotion on abrupt surfaces requires fusing proprioceptive and exteroceptive perception for explosive movements. In this paper, we propose SF-TIM (Simple Framework combining Terrain Imagination and Measurement), a single-policy method that enhances quadrupedal robot jumping agility, while preserving their fundamental blind walking capabilities. In addition, we introduce a terrain-guided reward design specifically to assist quadrupedal robots in high jumping, improving their performance in this task. To narrow the simulation-to-reality gap in quadrupedal robot learning, we introduce a stable and high-speed elevation map generation framework, enabling zero-shot simulation-to-reality transfer of locomotion ability. Our algorithm has been deployed and validated on both the small-/large-size quadrupedal robots, demonstrating its effectiveness in real-world applications: the robot has successfully traversed various high platform and gaps, showing the robustness of our proposed approach. The demo video is available here https://flysoaryun.github.io/SF-TIM.
@article{wang2024sftim,
title={SF-TIM: A Simple Framework for Enhancing Quadrupedal Robot Jumping Agility by Combining Terrain Imagination and Measurement},
author={Ze Wang and Yang Li and Long Xu and Hao Shi and Zunwang Ma and Zhen Chu and Chao Li and Fei Gao and Kailun Yang and Kaiwei Wang},
journal={arXiv preprint arXiv:2408.00486},
year={2024}
eprint={2408.00486},
archivePrefix={arXiv},
primaryClass={cs.RO}
}