Abstract
Achieving agile and generalized legged locomotion across terrains requires tight integration of perception and control, especially under occlusions and sparse footholds. Existing methods have demonstrated agility on parkour courses but often rely on end-to-end sensorimotor models with limited generalization and interpretability. By contrast, methods targeting generalized locomotion typically exhibit limited agility and struggle with visual occlusions. We introduce AME-2, a unified reinforcement learning (RL) framework for agile and generalized locomotion that incorporates a novel attention-based map encoder in the control policy. This encoder extracts local and global mapping features and uses attention mechanisms to focus on salient regions, producing an interpretable and generalized embedding for RL-based control. We further propose a learning-based mapping pipeline that provides fast, uncertainty-aware terrain representations robust to noise and occlusions, serving as policy inputs. It uses neural networks to convert depth observations into local elevations with uncertainties, and fuses them with odometry. The pipeline also integrates with parallel simulation so that we can train controllers with online mapping, aiding sim-to-real transfer. We validate AME-2 with the proposed mapping pipeline on a quadruped and a biped robot, and the resulting controllers demonstrate strong agility and generalization to unseen terrains in simulation and in real-world experiments.
Abstract (translated)
实现各种地形上敏捷且通用的腿式移动需要感知和控制之间的紧密集成，尤其是在存在遮挡和稀疏支撑点的情况下。现有的方法已经在障碍赛道上展示了敏捷性，但往往依赖于端到端的感觉运动模型，这些模型在泛化能力和可解释性方面表现有限。相比之下，专注于通用机动性的方法通常表现出较低的敏捷性和处理视觉遮挡的能力较弱。我们提出了AME-2，这是一种统一的强化学习（RL）框架，旨在实现既敏捷又通用的移动方式，并在控制策略中引入了一种新的基于注意力的地图编码器。该编码器提取局部和全局地图特征，并利用注意机制聚焦于显著区域，生成可解释且具有泛化的嵌入式表示用于基于RL的控制。 我们还提出了一种学习驱动的地图构建流水线，它提供快速、不确定度感知地形表征，能够有效应对噪声和遮挡问题，作为政策输入。该流水线使用神经网络将深度观察转换为带有不确定性评估的局部高度，并与里程计数据进行融合。此外，此流程可与并行模拟相结合，以便可以在在线地图构建过程中训练控制器，从而帮助实现仿真到现实环境中的迁移。 我们在四足和双足机器人上通过提出的地图流水线验证了AME-2的有效性，结果表明由此生成的控制器在仿真中以及实际实验中对未知地形表现出强大的敏捷性和泛化能力。
URL
https://arxiv.org/abs/2601.08485
