基于深度神经网络从演示中学习社交导航
Learning Social Navigation from Demonstrations with Deep Neural Networks
AI总结：
针对机器人社交导航需求，本研究利用深度学习在全局和局部规划层面的进展，训练两个分别对应不同目标的深度模型并应用于仿真机器人，实现了性能优于前馈神经网络的人类感知导航。
摘要：
传统路径规划技术将人类视为障碍物。随着机器人开始进入人类环境，这种情况已经发生了改变。在现代机器人上，社交导航已成为导航系统的重要组成部分。要使用基于学习的技术实现社交导航，需要一个能够以尽可能少的数据表示复杂函数的强大框架。在本研究中，我们利用深度学习在全局和局部规划层面的最新进展，在仿真机器人上实现了具备人类感知能力的导航。我们训练了两个具有不同目标的深度模型：一个用于全局规划，另一个用于局部规划，随后将这些模型应用于仿真机器人中。最终结果表明，我们的模型能够成功执行全局和局部规划任务。我们证实，与前馈神经网络相比，我们的系统能够生成成功到达目标同时避开障碍物的路径，且性能更优。
英文摘要：
Traditional path-planning techniques treat humans as obstacles. This has changed since robots started to enter human environments. On modern robots, social navigation has become an important aspect of navigation systems. To use learning-based techniques to achieve social navigation, a powerful framework that is capable of representing complex functions with as few data as possible is required. In this study, we benefited from recent advances in deep learning at both global and local planning levels to achieve human-aware navigation on a simulated robot. Two distinct deep models are trained with respective objectives: one for global planning and one for local planning. These models are then employed in the simulated robot. In the end, it has been shown that our model can successfully carry out both global and local planning tasks. We have shown that our system could generate paths that successfully reach targets while avoiding obstacles with better performance compared to feed-forward neural networks.
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。
