  Simulation to Real-World Transfer allows affordable and fast training of
learning-based robots for manipulation tasks using Deep Reinforcement Learning
methods. Currently, Sim2Real uses Asymmetric Actor-Critic approaches to reduce
the rich idealized features in simulation to the accessible ones in the real
world. However, the feature reduction from the simulation to the real world is
conducted through an empirically defined one-step curtail. Small feature
reduction does not sufficiently remove the actor's features, which may still
cause difficulty setting up the physical system, while large feature reduction
may cause difficulty and inefficiency in training. To address this issue, we
proposed Curriculum-based Sensing Reduction to enable the actor to start with
the same rich feature space as the critic and then get rid of the
hard-to-extract features step-by-step for higher training performance and
better adaptation for real-world feature space. The reduced features are
replaced with random signals from a Deep Random Generator to remove the
dependency between the output and the removed features and avoid creating new
dependencies. The methods are evaluated on the Allegro robot hand in a
real-world in-hand manipulation task. The results show that our methods have
faster training and higher task performance than baselines and can solve
real-world tasks when selected tactile features are reduced.
