面向基于DRL导航系统的进化课程训练
Evolutionary Curriculum Training for DRL-Based Navigation Systems
AI总结：
本文提出进化课程训练方法，通过评估DRL碰撞避免模型在结构化环境中的能力并生成针对性课程来提升其导航与避障性能，在五个环境中验证了更高成功率和更低碰撞次数。
摘要：
近年来，深度强化学习（DRL）已成为一种有前景的机器人碰撞避免方法。然而，此类DRL模型通常存在局限性，例如难以有效适应包含各种行人的结构化环境。为解决这一困难，以往研究尝试了若干方法，包括通过将航点规划器与DRL集成来训练端到端解决方案，以及开发多模态解决方案以缓解DRL模型的不足。然而，这些方法遇到了训练时间缓慢、可扩展性挑战以及不同模型之间协调性差等问题。为应对这些挑战，本文引入了一种称为进化课程训练的新方法。进化课程训练的主要目标是评估碰撞避免模型在各种场景中的能力，并创建课程以增强其不足的技能。本文提出了一种创新的评估技术，用于评估DRL模型在结构化地图中导航和避开动态障碍物的性能。此外，一个进化训练环境会生成所有课程，以改进DRL模型在先前评估中暴露出的不足技能。我们在五个结构化环境中对所提模型的性能进行了基准测试，以验证该进化训练环境能够带来更高成功率和更低平均碰撞次数的假设。更多细节和结果见我们的项目网站。
英文摘要：
In recent years, Deep Reinforcement Learning (DRL) has emerged as a promising method for robot collision avoidance. However, such DRL models often come with limitations, such as adapting effectively to structured environments containing various pedestrians. In order to solve this difficulty, previous research has attempted a few approaches, including training an end-to-end solution by integrating a waypoint planner with DRL and developing a multimodal solution to mitigate the drawbacks of the DRL model. However, these approaches have encountered several issues, including slow training times, scalability challenges, and poor coordination among different models. To address these challenges, this paper introduces a novel approach called evolutionary curriculum training to tackle these challenges. The primary goal of evolutionary curriculum training is to evaluate the collision avoidance model's competency in various scenarios and create curricula to enhance its insufficient skills. The paper introduces an innovative evaluation technique to assess the DRL model's performance in navigating structured maps and avoiding dynamic obstacles. Additionally, an evolutionary training environment generates all the curriculum to improve the DRL model's inadequate skills tested in the previous evaluation. We benchmark the performance of our model across five structured environments to validate the hypothesis that this evolutionary training environment leads to a higher success rate and a lower average number of collisions. Further details and results at our project website.
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。
