As robots become more common for both able-bodied individuals and those living with a disability, it is increasingly important that lay people be able to drive multi-degree-of-freedom platforms with low-dimensional controllers. One approach is to use state-conditioned action mapping methods to learn mappings between low-dimensional controllers and high DOF manipulators -- prior research suggests these mappings can simplify the teleoperation experience for users. Recent works suggest that neural networks predicting a local linear function are superior to the typical end-to-end multi-layer perceptrons because they allow users to more easily undo actions, providing more control over the system. However, local linear models assume actions exist on a linear subspace and may not capture nuanced actions in training data. We observe that the benefit of these mappings is being an odd function concerning user actions, and propose end-to-end nonlinear action maps which achieve this property. Unfortunately, our experiments show that such modifications offer minimal advantages over previous solutions. We find that nonlinear odd functions behave linearly for most of the control space, suggesting architecture structure improvements are not the primary factor in data-driven teleoperation. Our results suggest other avenues, such as data augmentation techniques and analysis of human behavior, are necessary for action maps to become practical in real-world applications, such as in assistive robotics to improve the quality of life of people living with w disability.

翻译：随着机器人技术日益普及于健全人群及残障人士群体，让非专业人员能够通过低维控制器操控多自由度平台变得愈发重要。一种解决方案是利用状态条件化动作映射方法，学习低维控制器与高自由度机械臂之间的映射关系——先前研究表明此类映射可简化用户的遥操作体验。近期研究指出，预测局部线性函数的神经网络优于传统的端到端多层感知机，因其允许用户更轻松地撤销动作，从而提供更强的系统控制能力。然而，局部线性模型假设动作存在于线性子空间，可能无法捕捉训练数据中的细微动作特征。我们观察到此类映射的优势在于其关于用户动作呈现奇函数特性，并据此提出具备该性质的端到端非线性动作映射方法。遗憾的是，实验表明此类改进相较于既有方案的提升有限。我们发现非线性奇函数在大部分控制空间中呈现线性行为，这表明架构结构改进并非数据驱动遥操作的核心因素。研究结果提示，需通过数据增强技术、人类行为分析等其他途径，才能使动作映射在实际应用（如辅助机器人领域）中发挥效用，从而提升残障人士的生活质量。