Developing bipedal robots capable of traversing diverse real-world terrains presents a fundamental robotics challenge, as existing methods using predefined height maps and static environments fail to address the complexity of unstructured landscapes. To bridge this gap, we propose GenTe, a framework for generating physically realistic and adaptable terrains to train generalizable locomotion policies. GenTe constructs an atomic terrain library that includes both geometric and physical terrains, enabling curriculum training for reinforcement learning-based locomotion policies. By leveraging function-calling techniques and reasoning capabilities of Vision-Language Models (VLMs), GenTe generates complex, contextually relevant terrains from textual and graphical inputs. The framework introduces realistic force modeling for terrain interactions, capturing effects such as soil sinkage and hydrodynamic resistance. To the best of our knowledge, GenTe is the first framework that systemically generates simulation environments for legged robot locomotion control. Additionally, we introduce a benchmark of 100 generated terrains. Experiments demonstrate improved generalization and robustness in bipedal robot locomotion.

本研究解决了现有方法无法有效应对复杂非结构化地形的问题，提出了GenTe框架，用于生成物理真实且可适应的地形，以训练可泛化的运动策略。通过使用视觉语言模型（VLM）的功能调用技术，GenTe能够根据文本和图形输入生成复杂的地形，显著提高了双足机器人运动的泛化能力和鲁棒性。

GenTe：用于通用四足机器人运动控制的生成真实世界地形