Gallant：基于体素网格的仿人机器人在三维受限地形中的运动与局部导航
Gallant: Voxel Grid-based Humanoid Locomotion and Local-navigation across 3D Constrained Terrains
AI总结：
针对现有仿人机器人感知模块无法捕捉完整三维环境结构的问题，本文提出基于体素网格的Gallant框架，利用体素化LiDAR数据和z分组二维CNN实现端到端优化，在多种三维受限地形中突破传统方法限制并取得近100%的挑战性场景成功率。
摘要：
鲁棒的仿人机器人运动需要对周围三维环境进行准确且全局一致的感知。然而，现有感知模块主要基于深度图像或高程图，仅能提供环境的局部、扁平化视图，无法捕捉完整三维结构。本文提出Gallant，一种基于体素网格的仿人机器人在三维受限地形中运动与局部导航的框架。该框架利用体素化LiDAR数据作为轻量、结构化的感知表示，并采用z分组二维CNN将该表示映射到控制策略，实现完全端到端优化。为支持可扩展的LiDAR训练并确保仿真到现实的一致性，本文开发了动态生成真实观测的高保真LiDAR仿真系统。实验结果表明，Gallant更广泛的感知覆盖使单一策略突破了以往方法局限于地面障碍物的限制，可应对侧向杂乱、头顶约束、多层结构和狭窄通道；通过改进端到端优化，Gallant首次在爬楼梯、踏上高架平台等挑战性场景中实现近100%的成功率。
英文摘要：
Robust humanoid locomotion requires accurate and globally consistent perception of the surrounding 3D environment. However, existing perception modules, mainly based on depth images or elevation maps, offer only partial and locally flattened views of the environment, failing to capture the full 3D structure. This paper presents Gallant, a voxel-grid-based framework for humanoid locomotion and local navigation in 3D constrained terrains. It leverages voxelized LiDAR data as a lightweight and structured perceptual representation, and employs a z-grouped 2D CNN to map this representation to the control policy, enabling fully end-to-end optimization. A high-fidelity LiDAR simulation that dynamically generates realistic observations is developed to support scalable, LiDAR-based training and ensure sim-to-real consistency. Experimental results show that Gallant's broader perceptual coverage facilitates the use of a single policy that goes beyond the limitations of previous methods confined to ground-level obstacles, extending to lateral clutter, overhead constraints, multi-level structures, and narrow passages. Gallant also firstly achieves near 100% success rates in challenging scenarios such as stair climbing and stepping onto elevated platforms through improved end-to-end optimization.
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。
