  Path planning is an important topic in robotics. Recently, value iteration
based deep learning models have achieved good performance such as Value
Iteration Network(VIN). However, previous methods suffer from slow convergence
and low accuracy on large maps, hence restricted in path planning for agents
with complex kinematics such as legged robots. Therefore, we propose a new
value iteration based path planning method called Capability Iteration
Network(CIN). CIN utilizes sparse reward maps and encodes the capability of the
agent with state-action transition probability, rather than a convolution
kernel in previous models. Furthermore, two training methods including
end-to-end training and training capability module alone are proposed, both of
which speed up convergence greatly. Several path planning experiments in
various scenarios, including on 2D, 3D grid world and real robots with
different map sizes are conducted. The results demonstrate that CIN has higher
accuracy, faster convergence, and lower sensitivity to random seed compared to
previous VI-based models, hence more applicable for real robot path planning.
