We present an optimal posture formulation and posture evaluation machanism for robotic recovery on uneven terrain and a novel teacher-student reinforcement learning framework that enables a student policy to infer external terrain conditions using only proprioceptive sensing.
We show that our policy performs on the non-flat environments, also we provide the pt that anyone could be free to download it and deploy it on the real robot.
Roots environments
Stairs
Mass
We show that our policy performs on the non-flat environments with Kyon robot which is Wheeled-Quadruped.
@ARTICLE{11495090,
author={Deng, Boyuan and Yang, Xu and Mo, Yilin and Tsagarakis, Nikolaos},
journal={IEEE Robotics and Automation Letters},
title={Teacher-Guided Terrain-Aware Learning for Recovery of Quadruped Robots},
year={2026},
volume={11},
number={6},
pages={7564-7571},
keywords={Quadrupedal robots;reinforcement learning;robot learning},
doi={10.1109/LRA.2026.3688056}}