VertiSelector：面向垂直挑战地形的轮式移动自动课程学习
VertiSelector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terrain
AI总结：
本文提出自动课程学习框架VertiSelector，通过优先采样高TD误差的垂直挑战地形，在VW-Chrono仿真和Verti-4-Wheeler平台上将成功率提升23.08%，显著提高样本效率和现实泛化能力。
摘要：
强化学习（RL）有潜力通过模拟的端到端试错学习经验来绕过复杂的运动动力学建模、规划和控制，从而实现极端的越野移动能力。然而，大多数RL方法在大量人工设计的仿真环境中训练时样本效率较低，并且难以泛化到现实世界。为了解决这些问题，我们提出了VertiSelector（VS），这是一个自动课程学习框架，旨在通过有选择地采样训练地形来提高学习效率和泛化能力。VS在重新访问具有较高时序差分（TD）误差的垂直挑战地形时优先对其进行采样，从而使机器人能够在其不断发展的能力边缘进行学习。通过动态调整采样重点，VS在基于Chrono多物理引擎构建的VW-Chrono仿真器中显著提升了样本效率和泛化能力。此外，我们提供了在Verti-4-Wheeler平台上使用VS的仿真和物理实验结果。这些结果表明，VS通过在训练过程中高效采样并稳健地泛化到现实世界，能够在成功率方面实现23.08%的提升。
英文摘要：
Reinforcement Learning (RL) has the potential to enable extreme off-road mobility by circumventing complex kinodynamic modeling, planning, and control by simulated end-to-end trial-and-error learning experiences. However, most RL methods are sample-inefficient when training in a large amount of manually designed simulation environments and struggle at generalizing to the real world. To address these issues, we introduce VertiSelector (VS), an automatic curriculum learning framework designed to enhance learning efficiency and generalization by selectively sampling training terrain. VS prioritizes vertically challenging terrain with higher Temporal Difference (TD) errors when revisited, thereby allowing robots to learn at the edge of their evolving capabilities. By dynamically adjusting the sampling focus, VS significantly boosts sample efficiency and generalization within the VW-Chrono simulator built on the Chrono multi-physics engine. Furthermore, we provide simulation and physical results using VS on a Verti-4-Wheeler platform. These results demonstrate that VS can achieve 23.08% improvement in terms of success rate by efficiently sampling during training and robustly generalizing to the real world.
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。
