  Tactile predictive models can be useful across several robotic manipulation
tasks, e.g. robotic pushing, robotic grasping, slip avoidance, and in-hand
manipulation. However, available tactile prediction models are mostly studied
for image-based tactile sensors and there is no comparison study indicating the
best performing models. In this paper, we presented two novel data-driven
action-conditioned models for predicting tactile signals during real-world
physical robot interaction tasks (1) action condition tactile prediction and
(2) action conditioned tactile-video prediction models. We use a magnetic-based
tactile sensor that is challenging to analyse and test state-of-the-art
predictive models and the only existing bespoke tactile prediction model. We
compare the performance of these models with those of our proposed models. We
perform the comparison study using our novel tactile-enabled dataset containing
51,000 tactile frames of a real-world robotic manipulation task with 11
flat-surfaced household objects. Our experimental results demonstrate the
superiority of our proposed tactile prediction models in terms of qualitative,
quantitative and slip prediction scores.
