论文标题
在机器人任务执行过程中对未定义的行为的补偿，通过切换控制器，取决于RNN中的嵌入式动力学
Compensation for undefined behaviors during robot task execution by switching controllers depending on embedded dynamics in RNN
论文作者
论文摘要
机器人应用需要正确的任务绩效和不确定行为的赔偿。尽管深度学习是执行复杂任务的一种有前途的方法，但对训练数据集中未反映的不确定行为的反应仍然具有挑战性。在人类机器人的协作任务中，机器人可能会因冲突和其他意外事件而采取意外的姿势。因此，机器人应该能够从干扰中恢复以完成预期任务的执行。我们通过在两个控制器之间切换不确定的行为提出了一种补偿方法。具体而言，提出的方法在基于学习的基于学习的控制器和基于模型的控制器之间切换，具体取决于学习任务动态的经常性神经网络的内部表示。我们将所提出的方法应用于采摘任务，并评估了未定义行为的补偿。模拟和实际机器人的实验结果证明了该方法的有效性和高性能。
Robotic applications require both correct task performance and compensation for undefined behaviors. Although deep learning is a promising approach to perform complex tasks, the response to undefined behaviors that are not reflected in the training dataset remains challenging. In a human-robot collaborative task, the robot may adopt an unexpected posture due to collisions and other unexpected events. Therefore, robots should be able to recover from disturbances for completing the execution of the intended task. We propose a compensation method for undefined behaviors by switching between two controllers. Specifically, the proposed method switches between learning-based and model-based controllers depending on the internal representation of a recurrent neural network that learns task dynamics. We applied the proposed method to a pick-and-place task and evaluated the compensation for undefined behaviors. Experimental results from simulations and on a real robot demonstrate the effectiveness and high performance of the proposed method.
