A bimanual shared-control method enables novice users to control a robot in real time to complete complex manipulation tasks. Human-centered environments provide affordances for and require the use of two-handed, or bimanual, manipulations. Robots designed to function in, and physically interact with, these environments have not been able to meet these requirements because standard bimanual control approaches have not accommodated the diverse, dynamic, and intricate coordinations between two arms to complete bimanual tasks. In this work, we enabled robots to more effectively perform bimanual tasks by introducing a bimanual shared-control method. The control method moves the robot’s arms to mimic the operator’s arm movements but provides on-the-fly assistance to help the user complete tasks more easily. Our method used a bimanual action vocabulary, constructed by analyzing how people perform two-hand manipulations, as the core abstraction level for reasoning about how to assist in bimanual shared autonomy. The method inferred which individual action from the bimanual action vocabulary was occurring using a sequence-to-sequence recurrent neural network architecture and turned on a corresponding assistance mode, signals introduced into the shared-control loop designed to make the performance of a particular bimanual action easier or more efficient. We demonstrate the effectiveness of our method through two user studies that show that novice users could control a robot to complete a range of complex manipulation tasks more successfully using our method compared to alternative approaches. We discuss the implications of our findings for real-world robot control scenarios.

中文翻译：

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基于共享控制的双向机器人操纵

双向共享控制方法使新手用户可以实时控制机器人以完成复杂的操纵任务。以人为中心的环境提供了能力，并且需要使用双手或双手操作。设计用于在这些环境中工作并与其物理交互的机器人无法满足这些要求，因为标准的双向控制方法无法适应两个手臂之间完成多样化的双向任务的多样化，动态和复杂的协调。在这项工作中，我们通过引入双向共享控制方法使机器人能够更有效地执行双向任务。该控制方法可以移动机器人的手臂以模仿操作员的手臂运动，但可以提供即时帮助以帮助用户更轻松地完成任务。我们的方法使用的是双向动作词汇，通过分析人们如何执行双手操作而构建，作为推理如何协助双手共享自主的核心抽象层。该方法使用序列到序列的递归神经网络体系结构，从双向动作词汇中推断出正在发生的单个动作，并打开了相应的辅助模式，将信号引入到共享控制环中，以设计特定的双向动作更容易或更有效。我们通过两次用户研究证明了我们方法的有效性，这表明与使用替代方法相比，新手用户可以使用我们的方法控制机器人来更成功地完成一系列复杂的操纵任务。我们讨论了我们的发现对现实机器人控制场景的影响。