DreamFLEX：为复杂地形异常情况学习故障感知四足机器人运动控制器
DreamFLEX: Learning Fault-Aware Quadrupedal Locomotion Controller for Anomaly Situation in Rough Terrains
AI总结：
针对四足机器人在复杂地形因硬件故障导致运动失败的问题，提出集成显式故障估计与调制网络的DreamFLEX控制器，实现故障下复杂环境穿越，在仿真和真实场景中性能优于现有方法。
摘要：
四足机器人领域的最新进展展现出令人印象深刻的敏捷性和穿越多种地形的能力。然而，在长距离行走或穿越复杂地形时，可能出现电机过热或关节锁死等硬件问题，导致运动失败。尽管已有多项研究为四足机器人提出容错控制方法，但在穿越非结构化地形方面仍存在挑战。本文提出DreamFLEX，一种鲁棒的容错运动控制器，能让四足机器人在关节故障条件下仍可穿越复杂环境。DreamFLEX集成了显式故障估计与调制网络，该网络联合估计机器人的关节故障向量，并利用此信息实时调整运动模式以适应故障条件，使四足机器人在复杂地形中保持稳定性和性能。实验结果表明，DreamFLEX在仿真和真实场景中均优于现有方法，能有效处理硬件故障并维持鲁棒的运动性能。
英文摘要：
Recent advances in quadrupedal robots have demonstrated impressive agility and the ability to traverse diverse terrains. However, hardware issues, such as motor overheating or joint locking, may occur during long-distance walking or traversing through rough terrains leading to locomotion failures. Although several studies have proposed fault-tolerant control methods for quadrupedal robots, there are still challenges in traversing unstructured terrains. In this paper, we propose DreamFLEX, a robust fault-tolerant locomotion controller that enables a quadrupedal robot to traverse complex environments even under joint failure conditions. DreamFLEX integrates an explicit failure estimation and modulation network that jointly estimates the robot's joint fault vector and utilizes this information to adapt the locomotion pattern to faulty conditions in real-time, enabling quadrupedal robots to maintain stability and performance in rough terrains. Experimental results demonstrate that DreamFLEX outperforms existing methods in both simulation and real-world scenarios, effectively managing hardware failures while maintaining robust locomotion performance.
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。
