用AI生成逼真地形，训练通用双足机器人行走能力
GenTe: Generative Real-world Terrains for General Legged Robot Locomotion Control
- 通过视觉语言模型输入文本/图像生成复杂地形
- 构建100个地形基准测试集，提升机器人泛化能力
- 适合研究机器人运动控制与仿真环境构建的学者
实现双足机器人在多样化真实地形上的自主行走是机器人领域的核心挑战。现有方法依赖预设高程图和静态环境，难以应对非结构化地形的复杂性。为此，我们提出GenTe框架，系统生成物理上真实且可适应的地形，用于训练通用运动策略。GenTe构建包含几何与物理特性的基础地形库，支持强化学习中的课程训练。利用视觉-语言模型（VLM）的函数调用与推理能力，从文本和图形输入中生成上下文相关的复杂地形。框架引入真实的力建模机制，捕捉土壤沉陷、流体阻力等交互效应。据我们所知，GenTe是首个系统性生成足式机器人运动控制仿真环境的框架。同时，我们构建了包含100个生成地形的基准测试集。实验表明，该方法显著提升了双足机器人在复杂地形中的泛化性与鲁棒性。
原文摘要 · Abstract (English)
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.
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