Enabling robots to autonomously navigate complex environments is essential
for real-world deployment. Prior methods approach this problem by having the
robot maintain an internal map of the world, and then use a localization and
planning method to navigate through the internal map. However, these approaches
often include a variety of assumptions, are computationally intensive, and do
not learn from failures. In contrast, learning-based methods improve as the
robot acts in the environment, but are difficult to deploy in the real-world
due to their high sample complexity. To address the need to learn complex
policies with few samples, we propose a generalized computation graph that
subsumes value-based model-free methods and model-based methods, with specific
instantiations interpolating between model-free and model-based. We then
instantiate this graph to form a navigation model that learns from raw images
and is sample efficient. Our simulated car experiments explore the design
decisions of our navigation model, and show our approach outperforms
single-step and $N$-step double Q-learning. We also evaluate our approach on a
real-world RC car and show it can learn to navigate through a complex indoor
environment with a few hours of fully autonomous, self-supervised training.
Videos of the experiments and code can be found at github.com/gkahn13/gcg

提出一种广义计算图，包含基于价值的无模型方法和基于模型的方法，通过学习从原始图像中学习导航模型，实现样本高效，并利用模拟实验和实际应用验证了新方法的优越性。