Hamilton Jacobi Value Iteration Network

Abstract

In this work, we address the safe navigation problem for the robot equipped with the neural network controller. Our goal is to propose a neural network controller representation that can efficiently and safely learn a safe policy. By following the learned safe policy, the robot can reach the goal state while avoiding hitting obstacles and walls all the time. We use Hamilton Jacobi safety analysis to improve the safety awareness of the policy and integrate it within the value iteration network to generalize to the new, unseen domains outside the training set. Applying the transfer learning techniques, we can learn a reward function that maps each state to a reasonable reward value. We use the learned reward function to construct the unknown part in the discrete-time Hamilton Jacobi value function and integrate this Hamilton Jacobi value function into the value iteration network to construct our Hamilton Jacobi value iteration network model. Finally, we compare the performance of our model with the value iteration network model in the grid world domains to show our model can safely learn a safe policy that generalizes to the new, unseen domains.