用渐进式学习让机器人听懂复杂指令并自主导航。
Mobile Robots through Task-Based Human Instructions using Incremental Curriculum Learning
- 通过模拟人类学习过程设计渐进任务难度
- 训练效率提升，导航成功率显著提高
- 适合研究人机协作与智能机器人导航的读者
本文将增量式课程学习（ICL）与深度强化学习（DRL）结合，用于实现移动机器人基于任务型人类指令的自主导航。通过构建类似人类学习过程的渐进式任务难度曲线，该方法系统性提升机器人对复杂指令的理解与执行能力。研究分析了DRL与ICL的协同机制，表明该组合不仅提高了训练效率，还增强了机器人在动态室内环境中泛化导航的能力。实验证明，采用ICL增强的DRL框架训练的机器人，在指令理解与路径规划上均优于无课程学习的基线模型，验证了结构化学习进程在机器人训练中的有效性。
原文摘要 · Abstract (English)
This paper explores the integration of incremental curriculum learning (ICL) with deep reinforcement learning (DRL) techniques to facilitate mobile robot navigation through task-based human instruction. By adopting a curriculum that mirrors the progressive complexity encountered in human learning, our approach systematically enhances robots' ability to interpret and execute complex instructions over time. We explore the principles of DRL and its synergy with ICL, demonstrating how this combination not only improves training efficiency but also equips mobile robots with the generalization capability required for navigating through dynamic indoor environments. Empirical results indicate that robots trained with our ICL-enhanced DRL framework outperform those trained without curriculum learning, highlighting the benefits of structured learning progressions in robotic training.
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