Shared Multi-Task Imitation Learning for Indoor Self-Navigation
Shared Multi-Task Imitation Learning for Indoor Self-Navigation
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DOI：
10.1109/glocom.2018.8647614
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发表时间：
2018-08
期刊：
影响因子：
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通讯作者：
Junhong Xu;Qiwei Liu;Hanqing Guo;Aaron Kageza;Saeed AlQarni;Shaoen Wu
中科院分区：
文献类型：
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作者：
Junhong Xu;Qiwei Liu;Hanqing Guo;Aaron Kageza;Saeed AlQarni;Shaoen Wu
深度模仿学习使机器人能够从专家演示中学习，以执行诸如车道跟随或避障等任务。然而，在传统的模仿学习框架中，一个模型只学习一个任务，因此缺乏支持机器人在室内环境中使用一个模型执行各种不同导航任务的能力。本文提出了一个新的框架——共享多头模仿学习（SMIL），该框架允许机器人使用一个模型执行多个任务，而无需在不同的模型之间切换。我们将每个任务建模为一个子策略，并设计了一个多头策略，通过汇总所有子策略的激活来学习相关任务之间的共享信息。与单个或非共享多头策略相比，该框架能够利用任务之间的相关信息来提高性能。我们使用基于NVIDIA TX2的机器人实现了该框架，并在不同基线解决方案的室内环境中进行了广泛的实验。结果表明，SMIL策略的性能比非共享多头策略提高了一倍。
Deep imitation learning enables robots to learn from expert demonstrations to perform tasks such as lane following or obstacle avoidance. However, in the traditional imitation learning framework, one model only learns one task, and thus it lacks of the capability to support a robot to perform various different navigation tasks with one model in indoor environments. This paper proposes a new framework, Shared Multi-headed Imitation Learning (SMIL), that allows a robot to perform multiple tasks with one model without switching among different models. We model each task as a sub-policy and design a multi-headed policy to learn the shared information among related tasks by summing up activations from all sub-policies. Compared to single or non-shared multi-headed policies, this framework is able to leverage correlated information among tasks to increase performance. We have implemented this framework using a robot based on NVIDIA TX2 and performed extensive experiments in indoor environments with different baseline solutions. The results demonstrate that SMIL has doubled the performance over non-shared multi-headed policy.