  Manipulation of deformable objects is a challenging task for a robot. It will
be problematic to use a single sensory input to track the behaviour of such
objects: vision can be subjected to occlusions, whereas tactile inputs cannot
capture the global information that is useful for the task. In this paper, we
study the problem of using vision and tactile inputs together to complete the
task of following deformable linear objects, for the first time. We create a
Reinforcement Learning agent using different sensing modalities and investigate
how its behaviour can be boosted using visual-tactile fusion, compared to using
a single sensing modality. To this end, we developed a benchmark in simulation
for manipulating the deformable linear objects using multimodal sensing inputs.
The policy of the agent uses distilled information, e.g., the pose of the
object in both visual and tactile perspectives, instead of the raw sensing
signals, so that it can be directly transferred to real environments. In this
way, we disentangle the perception system and the learned control policy. Our
extensive experiments show that the use of both vision and tactile inputs,
together with proprioception, allows the agent to complete the task in up to
92% of cases, compared to 77% when only one of the signals is given. Our
results can provide valuable insights for the future design of tactile sensors
and for deformable objects manipulation.
