When working alongside human collaborators in dynamic and unstructured environments, such as disaster recovery or military operation, fast field adaptation is necessary for an unmanned ground vehicle (UGV) to perform its duties or learn novel tasks. In these scenarios, personnel and equipment are constrained, making training with minimal human supervision a desirable learning attribute. We address the problem of making UGVs more reliable and adaptable teammates with a novel framework that uses visual perception and inverse optimal control to learn traversal costs for environment features. Through extensive evaluation in a real-world environment, we show that our framework requires few human demonstrated trajectory exemplars to learn feature costs that reliably encode several different traversal behaviors. Additionally, we present an on-line version of the framework that allows a human teammate to intervene during live operation to correct deteriorated behavior or to adapt behavior to dynamic changes in complex and unstructured environments.
翻译:在动态和无结构的环境中,如灾后恢复或军事行动,与人类合作者一起工作时,无人驾驶地面飞行器(UGV)履行职责或学习新任务需要快速实地适应。在这些情况下,人员和设备受到限制,使最低限度的人力监督培训成为可取的学习属性。我们处理使UGV团队更可靠、更适应性更强的问题,其新框架利用视觉感知和反向最佳控制来学习环境特征的历程成本。通过在现实世界环境中的广泛评价,我们显示我们的框架需要很少人显示的轨道外表模型来学习可靠地记录几种不同轮廓行为的特质成本。此外,我们提供框架的在线版本,让人类团队在现场行动期间进行干预,以纠正退化的行为,或使行为适应复杂和无结构环境中的动态变化。