  In this paper, we propose a novel framework for tactile-based dexterous
manipulation learning with a blind anthropomorphic robotic hand, i.e. without
visual sensing. First, object-related states were extracted from the raw
tactile signals by a graph-based perception model - TacGNN. The resulting
tactile features were then utilized in the policy learning of an in-hand
manipulation task in the second stage. This method was examined by a Baoding
ball task - simultaneously manipulating two spheres around each other by 180
degrees in hand. We conducted experiments on object states prediction and
in-hand manipulation using a reinforcement learning algorithm (PPO). Results
show that TacGNN is effective in predicting object-related states during
manipulation by decreasing the RMSE of prediction to 0.096cm comparing to other
methods, such as MLP, CNN, and GCN. Finally, the robot hand could finish an
in-hand manipulation task solely relying on the robotic own perception -
tactile sensing and proprioception. In addition, our methods are tested on
three tasks with different difficulty levels and transferred to the real robot
without further training.
