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arXiv 2408.00486cs.RO

SF-TIM:结合地形想象与测量增强四足机器人跳跃敏捷性的简单框架

SF-TIM: A Simple Framework for Enhancing Quadrupedal Robot Jumping Agility by Combining Terrain Imagination and Measurement

State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, China(浙江大学现代光学仪器国家重点实验室);School of Artificial Intelligence and Robotics and National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, China(湖南大学人工智能与机器人学院和机器人视觉感知与控制技术国家工程研究中心);State Key Laboratory of Industrial Control Technology, Zhejiang University, China(浙江大学工业控制技术国家重点实验室);DeepRobotics Co. Ltd., China(深睿科技有限公司)

Ze Wang, Yang Li, Long Xu, Hao Shi, Zunwang Ma, Zhen Chu, Chao Li, Fei Gao, Kailun Yang, Kaiwei Wang

AI总结:

本文提出SF-TIM框架,通过结合地形想象与测量及地形引导奖励设计,增强四足机器人跳跃敏捷性并保留盲走能力,同时引入高程图生成框架实现零样本仿真到现实迁移,在大小型机器人上验证了有效性。

摘要:

在高位平台上动态跳跃和跨越沟隙使足式机器人区别于轮式机器人。与在粗糙地形上行走不同,在突变表面上的动态运动需要整合本体感知与外部感知,以实现爆发性动作。本文提出SF-TIM(结合地形想象与测量的简单框架),这是一种单策略方法,可增强四足机器人的跳跃敏捷性,同时保留其基本的盲走能力。此外,我们引入了地形引导的奖励设计,专门辅助四足机器人进行高处跳跃,提升其在此任务中的表现。为缩小四足机器人学习中的仿真到现实差距,我们引入了稳定且高速的高程图生成框架,实现了运动能力的零样本仿真到现实迁移。我们的算法已在小型和大型四足机器人上部署并验证,证明了其在实际应用中的有效性:机器人成功穿越了各种高位平台和沟隙,展示了所提方法的鲁棒性。演示视频可在https://flysoaryun.github.io/SF-TIM获取。

英文摘要:

Dynamic jumping on high platforms and over gaps differentiates legged robots from wheeled counterparts. Dynamic locomotion on abrupt surfaces, as opposed to walking on rough terrains, demands the integration of proprioceptive and exteroceptive perception to enable explosive movements. In this paper, we propose SF-TIM (Simple Framework combining Terrain Imagination and Measurement), a single-policy method that enhances quadrupedal robot jumping agility, while preserving their fundamental blind walking capabilities. In addition, we introduce a terrain-guided reward design specifically to assist quadrupedal robots in high jumping, improving their performance in this task. To narrow the simulation-to-reality gap in quadrupedal robot learning, we introduce a stable and high-speed elevation map generation framework, enabling zero-shot simulation-to-reality transfer of locomotion ability. Our algorithm has been deployed and validated on both the small-/large-size quadrupedal robots, demonstrating its effectiveness in real-world applications: the robot has successfully traversed various high platforms and gaps, showing the robustness of our proposed approach. A demo video has been made available at https://flysoaryun.github.io/SF-TIM.

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。

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