STATE-NAV：面向粗糙地形双足导航的稳定性感知可通行性估计
STATE-NAV: Stability-Aware Traversability Estimation for Bipedal Navigation on Rough Terrain
AI总结：
本文提出STATE-NAV框架，通过基于Transformer的神经网络预测双足机器人在粗糙地形中的稳定性，结合分层规划器实现风险感知的导航，提升导航性能和鲁棒性。
摘要：
双足机器人在 maneuvering 人类中心环境方面具有优势，但相较于轮式或四足机器人等稳定移动平台，面临更大的失败风险。尽管基于学习的可通行性已广泛研究这些平台，但双足可通行性却依赖于手动设计的规则，对粗糙地形上的运动稳定性考虑有限。在本工作中，我们提出了首个基于学习的可通行性估计和风险敏感导航框架，用于在多样、不规则环境中操作的双足机器人。TravFormer，一种基于Transformer的神经网络，被训练以预测双足不稳定性并带有不确定性，从而实现风险感知和自适应规划。基于该网络，我们定义可通行性为稳定性感知的指令速度——即保持不稳定性低于用户定义限制的最快指令速度。这种基于速度的可通行性被整合到一个分层规划器中，该规划器结合了基于可通行性信息的快速随机树星（TravRRT*）以实现时间高效的规划，以及模型预测控制（MPC）以实现安全执行。我们在MuJoCo模拟和现实世界中验证了我们的方法，证明了在各种地形上相比现有方法，导航性能得到改善，具有更强的鲁棒性和时间效率。
英文摘要：
Bipedal robots have advantages in maneuvering human-centered environments, but face greater failure risk compared to other stable mobile platforms such as wheeled or quadrupedal robots. While learning-based traversability has been widely studied for these platforms, bipedal traversability has instead relied on manually designed rules with limited consideration of locomotion stability on rough terrain. In this work, we present the first learning-based traversability estimation and risk-sensitive navigation framework for bipedal robots operating in diverse, uneven environments. TravFormer, a transformer-based neural network, is trained to predict bipedal instability with uncertainty, enabling risk-aware and adaptive planning. Based on the network, we define traversability as stability-aware command velocity-the fastest command velocity that keeps instability below a user-defined limit. This velocity-based traversability is integrated into a hierarchical planner that combines traversability-informed Rapid Random Tree Star (TravRRT*) for time-efficient planning and Model Predictive Control (MPC) for safe execution. We validate our method in MuJoCo simulation and the real world, demonstrating improved navigation performance, with enhanced robustness and time efficiency across varying terrains compared to existing methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
