Rakita Daniel, Mutlu Bilge, Gleicher Michael, Hiatt Laura M
Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA.
Naval Research Laboratory, Washington, DC, USA.
Sci Robot. 2019 May 29;4(30). doi: 10.1126/scirobotics.aaw0955.
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.
以人类为中心的环境为双手操作提供了便利条件，并且需要使用双手操作。设计用于在这些环境中运行并与其进行物理交互的机器人无法满足这些要求，因为标准的双手控制方法无法适应双臂之间多样化、动态且复杂的协调以完成双手任务。在这项工作中，我们通过引入一种双手共享控制方法，使机器人能够更有效地执行双手任务。该控制方法移动机器人的手臂以模仿操作员的手臂动作，但提供实时协助以帮助用户更轻松地完成任务。我们的方法使用通过分析人们如何进行双手操作构建的双手动作词汇表，作为推理如何协助双手共享自主性的核心抽象层次。该方法使用序列到序列循环神经网络架构推断双手动作词汇表中正在发生的单个动作，并开启相应的协助模式，这些信号被引入共享控制回路，旨在使特定双手动作的执行更轻松或更高效。我们通过两项用户研究证明了我们方法的有效性，研究表明与其他方法相比，新手用户使用我们的方法能够更成功地控制机器人完成一系列复杂的操作任务。我们讨论了我们的研究结果对现实世界机器人控制场景的影响。
