  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.
