  Tactile sensors provide information that can be used to learn and execute
manipulation tasks. Different tasks, however, might require different levels of
sensory information; which in turn likely affect learning rates and
performance. This paper evaluates the role of tactile information on autonomous
learning of manipulation with a simulated 3-finger tendon-driven hand. We
compare the ability of the same learning algorithm (Proximal Policy
Optimization, PPO) to learn two manipulation tasks (rolling a ball about the
horizontal axis with and without rotational stiffness) with three levels of
tactile sensing: no sensing, 1D normal force, and 3D force vector.
Surprisingly, and contrary to recent work on manipulation, adding 1D
force-sensing did not always improve learning rates compared to no
sensing---likely due to whether or not normal force is relevant to the task.
Nonetheless, even though 3D force-sensing increases the dimensionality of the
sensory input---which would in general hamper algorithm convergence---it
resulted in faster learning rates and better performance. We conclude that, in
general, sensory input is useful to learning only when it is relevant to the
task---as is the case of 3D force-sensing for in-hand manipulation against
gravity. Moreover, the utility of 3D force-sensing can even offset the added
computational cost of learning with higher-dimensional sensory input.
