SELFI：面向社交导航的基于强化学习的自主自我改进方法
SELFI: Autonomous Self-Improvement with Reinforcement Learning for Social Navigation
AI总结：
针对机器人社交导航的自主提升需求，提出融合离线基于模型学习与在线无模型强化学习的SELFI方法，可高效微调预训练策略，在真实环境中提升避障与社交合规行为表现，减少人工干预。
摘要：
能够通过交互和经验实现自我提升的自主机器人是机器人系统实际部署的关键。本文提出了一种在线学习方法SELFI，该方法利用机器人的在线经验，高效快速地微调预训练的控制策略。SELFI在离线基于模型的学习基础上应用在线无模型强化学习，以融合两种学习范式的优势。具体而言，SELFI将离线预训练中使用的同一基于模型的学习目标纳入在线无模型强化学习得到的Q值中，以此稳定在线学习过程。我们在多个真实环境中对SELFI进行了评估，结果显示其在避障方面有所提升，且通过人类用户研究衡量，其行为更符合社交规范。SELFI让我们能够在更少人工干预的情况下快速学习有用的机器人行为，例如针对行人的预判行为、对小型透明物体的避障，以及避免在不平整的地面上行驶。我们在项目页面提供了补充视频，以展示微调后策略的性能。
英文摘要：
Autonomous self-improving robots that interact and improve with experience are key to the real-world deployment of robotic systems. In this paper, we propose an online learning method, SELFI, that leverages online robot experience to rapidly fine-tune pre-trained control policies efficiently. SELFI applies online model-free reinforcement learning on top of offline model-based learning to bring out the best parts of both learning paradigms. Specifically, SELFI stabilizes the online learning process by incorporating the same model-based learning objective from offline pre-training into the Q-values learned with online model-free reinforcement learning. We evaluate SELFI in multiple real-world environments and report improvements in terms of collision avoidance, as well as more socially compliant behavior, measured by a human user study. SELFI enables us to quickly learn useful robotic behaviors with less human interventions such as pre-emptive behavior for the pedestrians, collision avoidance for small and transparent objects, and avoiding travel on uneven floor surfaces. We provide supplementary videos to demonstrate the performance of our fine-tuned policy on our project page.
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。
