MOVE：在3D环境中利用有限视野实现多技能全向腿部运动
MOVE: Multi-skill Omnidirectional Legged Locomotion with Limited View in 3D Environments
AI总结：
针对低成本四足机器人前向视野狭窄与外部感知噪声问题，提出端到端MOVE框架，融合伪孪生网络、监督与对比学习，实现有限视野下多技能全向运动。
摘要：
腿足机器人在穿越复杂3D地形方面具有固有优势。然而，以往关于配备自我中心视觉系统的低成本四足机器人的研究受到狭窄前向视野和外部感知噪声的限制，从而制约了此类环境中的全向移动能力。尽管通过层级结构构建体素地图可以改进外部感知处理，但这会带来显著的计算开销、噪声和延迟。本文提出MOVE，这是一种单阶段端到端学习框架，能够在3D环境中以有限视野实现多技能全向腿部运动，就像真实动物所能做到的那样。当运动方向与机器人视线一致时，外部感知会增强运动能力，使其能够完成极限攀爬和跳跃。当视觉受阻或运动方向位于机器人视野之外时，机器人依靠本体感觉完成爬行和爬楼梯等任务。我们通过引入结合监督学习与对比学习的伪孪生网络结构，将所有这些技能整合到单一神经网络中，该结构有助于机器人推断视野之外的周围环境。仿真和真实场景中的实验均证明了本方法的鲁棒性，拓宽了配备自我中心视觉的机器人的作业环境。
英文摘要：
Legged robots possess inherent advantages in traversing complex 3D terrains. However, previous work on low-cost quadruped robots with egocentric vision systems has been limited by a narrow front-facing view and exteroceptive noise, restricting omnidirectional mobility in such environments. While building a voxel map through a hierarchical structure can refine exteroception processing, it introduces significant computational overhead, noise, and delays. In this paper, we present MOVE, a one-stage end-to-end learning framework capable of multi-skill omnidirectional legged locomotion with limited view in 3D environments, just like what a real animal can do. When movement aligns with the robot's line of sight, exteroceptive perception enhances locomotion, enabling extreme climbing and leaping. When vision is obstructed or the direction of movement lies outside the robot's field of view, the robot relies on proprioception for tasks like crawling and climbing stairs. We integrate all these skills into a single neural network by introducing a pseudo-siamese network structure combining supervised and contrastive learning which helps the robot infer its surroundings beyond its field of view. Experiments in both simulations and real-world scenarios demonstrate the robustness of our method, broadening the operational environments for robotics with egocentric vision.
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。
