This work addresses human intention identification during physical Human-Robot Interaction (pHRI) tasks to include this information in an assistive controller. To this purpose, human intention is defined as the desired trajectory that the human wants to follow over a finite rolling prediction horizon so that the robot can assist in pursuing it. This work investigates a Recurrent Neural Network (RNN), specifically, Long-Short Term Memory (LSTM) cascaded with a Fully Connected layer. In particular, we propose an iterative training procedure to adapt the model. Such an iterative procedure is powerful in reducing the prediction error. Still, it has the drawback that it is time-consuming and does not generalize to different users or different co-manipulated objects. To overcome this issue, Transfer Learning (TL) adapts the pre-trained model to new trajectories, users, and co-manipulated objects by freezing the LSTM layer and fine-tuning the last FC layer, which makes the procedure faster. Experiments show that the iterative procedure adapts the model and reduces prediction error. Experiments also show that TL adapts to different users and to the co-manipulation of a large object. Finally, to check the utility of adopting the proposed method, we compare the proposed controller enhanced by the intention prediction with the other two standard controllers of pHRI.

翻译：本文针对物理人机交互任务中的人类意图识别问题展开研究，旨在将识别信息整合至辅助控制器中。为此，本文将人类意图定义为在有限滚动预测时域内人类期望执行的轨迹，从而使机器人能够协助完成该轨迹。本研究采用级联全连接层的循环神经网络，具体为长短期记忆网络模型。我们提出了一种迭代训练方法来适配模型：该迭代方法在降低预测误差方面表现优异，但存在耗时长且无法推广至不同用户或不同协同操作对象的缺陷。为克服该问题，迁移学习通过冻结LSTM层并微调末层全连接层来适配预训练模型，使其适应新轨迹、新用户及新协同操作对象，显著提升了流程速度。实验表明，迭代方法能够有效适配模型并降低预测误差；同时验证了迁移学习可适配不同用户及大型物体的协同操作。最终，为检验所提方法的实用性，我们将集成意图预测的增强型控制器与两种标准pHRI控制器进行了对比实验。