论文标题
机器人从人类演示中导航：带有环境特征图的学习控制行为
Robot navigation from human demonstration: learning control behaviors with environment feature maps
论文作者
论文摘要
当与人类合作者与动态和非结构化环境（例如灾难恢复或军事行动）一起工作时，快速场适应是无人接地车（UGV）即可履行职责或学习新任务。在这些情况下，人事和设备受到限制，从而使人类监督最少的培训成为理想的学习属性。我们解决了使UGV通过使用视觉感知和反向最佳控制的新颖框架来使UGV更可靠和适应能力的队友的问题，以学习环境特征的遍历成本。通过在实际环境中进行广泛的评估，我们表明我们的框架需要很少有人证明的轨迹示例来学习特征成本，这些成本可靠地编码几种不同的遍历行为。此外，我们提出了该框架的在线版本，该版本允许人类队友在实时操作期间进行干预以纠正恶化的行为或使行为适应复杂和非结构化环境中的动态变化。
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.
