Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Value iteration networks are an approximation of the value iteration (VI) algorithm implemented with convolutional neural networks to make VI fully differentiable.
In this work, we study these networks in the context of robot motion planning, with a focus on applications to planetary rovers.
The key challenging task in learning-based motion planning is to learn a transformation from terrain observations to a suitable navigation reward function.
In order to deal with complex terrain observations and policy learning, we propose a value iteration recurrence, referred to as the soft value iteration network (SVIN).
SVIN is designed to produce more effective training gradients through the value iteration network.
It relies on a soft policy model, where the policy is represented with a probability distribution over all possible actions, rather than a deterministic policy that returns only the best action.
We demonstrate the effectiveness of the proposed method in robot motion planning scenarios.
In particular, we study the application of SVIN to very challenging problems in planetary rover navigation and present early training results on data gathered by the  Curiosity rover that is currently operating on Mars.
Value iteration networks (VIN) are an approximation of the value iteration algorithm BID1 BID2 that were originally proposed by BID14 as a way of incorporating a differentiable planning component into a reinforcement learning architecture.
In this work, we apply the technique in a fully supervised, imitation learning approach for robot path planning problems.
The architecture has two main neural network components, the VIN itself which is an unrolling of the value iteration recurrence to a fixed number of iterations, and the reward network which transforms terrain and goal data into a reward map that feeds into the VIN.
An important feature of this approach is that the learned component of the network exists entirely within the reward network, the output of which must be a well behaved reward function for our planning problem, making human interpretation of the planning results relatively easy.
In this work, we restrict ourselves to 2D path planning problems on grids that have only local neighbors.
This is a natural constraint of using convolutional neural networks for implementing the value iteration algorithm, although one could imagine convolutional VINs at higher dimensionality.Although the use of a reward function in value iteration is quite intuitive, writing down how to calculate a reward function to produce the desired planning results is deceptively difficult, as has been observed by researchers in the past .
Part of the difficulty comes from the need to keep the relative costs and rewards of different types of terrain in balance with each other.
In this paper, we have developed a new variant of value iteration networks, referred to SVIN.
The focus of SVIN is to solve motion planning problems via imitation learning.
Our primary learning objective is to develop a network that can transform map data into a properly calibrated reward function.
The SVIN approach gives us the differentiable planning algorithm and an informative gradient calculation necessary to accomplish this goal.
We demonstrated this approach on a synthetic rockworld dataset, and showed some early results applying the technique to a much more challenging navigation problem with very noisy dataset for a rover navigation on Mars.In our future work, we will be looking at ways to improve the performance on the Mars dataset.
In particular, we will augment the Mars dataset with synthetic simulation data created from highfidelity Mars terrain and Mars rover simulators.
We will also experiment with using a pre-trained reward network trained on similar visual tasks the accelerate learning process.
Second, we will look into selecting correct sub-goals on rovers paths in the Mars dataset.
This will significantly reduce the noise in the dataset and enhance the learning results.
We propose an improvement to value iteration networks, with applications to planetary rover path planning.