The letter focuses on Haptic Glove (HG) based control of a Robotic Hand (RH) executing in-hand manipulation of certain objects of interest. The high dimensional motion signals in HG and RH possess intrinsic variability of kinematics resulting in difficulty to establish a direct mapping of the motion signals from HG onto the RH. An estimation mechanism is proposed to quantify the motion signal acquired from the human controller in relation to the intended goal pose of the object being held by the robotic hand. A control algorithm is presented to transform the synthesized intent at the RH and allow relocation of the object to the expected goal pose. The lag in synthesis of the intent in the presence of communication delay leads to a requirement of predicting the estimated intent. We leverage an attention-based convolutional neural network encoder to predict the trajectory of intent for a certain lookahead to compensate for the delays. The proposed methodology is evaluated across objects of different shapes, mass, and materials. We present a comparative performance of the estimation and prediction mechanisms on 5G-driven real-world robotic setup against benchmark methodologies. The test-MSE in prediction of human intent is reported to yield ~ 97.3 -98.7% improvement of accuracy in comparison to LSTM-based benchmark

翻译：本文聚焦于基于触觉手套（HG）控制机械手（RH）执行特定目标物体的手内操作。HG与RH中的高维运动信号具有运动学内在变异性，导致难以建立从HG到RH的直接运动信号映射。本文提出一种估计机制，用以量化从人类控制器采集的运动信号与机械手所持物体预期目标位姿之间的关系。提出一种控制算法，将合成后的预期意图转换至RH端，并允许物体重新定位至预期目标位姿。通信延迟导致意图合成滞后，因此需要预测已估计的意图。我们利用基于注意力机制的卷积神经网络编码器，对特定前瞻时间内的意图轨迹进行预测以补偿延迟。所提方法在不同形状、质量及材质的物体上进行评估。我们展示了在5G驱动的真实机器人平台上，该方法在估计与预测机制上与基准方法的性能对比。结果表明，与基于LSTM的基准方法相比，人类意图预测的测试均方误差（MSE）准确率提升约97.3%-98.7%。