Shared control-based bimanual robot manipulation
Shared control-based bimanual robot manipulation
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DOI：
10.1126/scirobotics.aaw0955
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发表时间：
2019-05-29
期刊：
影响因子：
25
通讯作者：
Hiatt, Laura M.
中科院分区：
文献类型：
--
作者：
Rakita, Daniel;Mutlu, Bilge;Hiatt, Laura M.
以人为中心的环境提供了启示，并需要使用双手，或双手，操纵。被设计为在这些环境中起作用并与这些环境物理交互的机器人已经不能满足这些要求，因为标准双手控制方法不能适应两个手臂之间的多样的、动态的和复杂的协调以完成双手任务。在这项工作中，我们使机器人更有效地执行双手任务，通过引入双手共享控制方法。该控制方法移动机器人的手臂以模仿操作员的手臂运动，但提供即时协助以帮助用户更轻松地完成任务。我们的方法使用了双手动作词汇，通过分析人们如何执行双手操作，作为核心抽象层次的推理如何协助双手共享自治。该方法使用序列到序列递归神经网络架构从双手动作词汇中推断出哪个个体动作正在发生，并打开相应的辅助模式，将信号引入共享控制回路，以使特定双手动作的执行更容易或更有效。我们证明了我们的方法的有效性，通过两个用户的研究表明，新手用户可以控制机器人完成一系列复杂的操作任务，更成功地使用我们的方法相比，替代方法。我们讨论了我们的研究结果对现实世界的机器人控制方案的影响。
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.