We consider the problem of spatial path planning. In contrast to the
classical solutions which optimize a new plan from scratch and assume access to
the full map with ground truth obstacle locations, we learn a planner from the
data in a differentiable manner that allows us to leverage statistical
regularities from past data. We propose Spatial Planning Transformers (SPT),
which given an obstacle map learns to generate actions by planning over
long-range spatial dependencies, unlike prior data-driven planners that
propagate information locally via convolutional structure in an iterative
manner. In the setting where the ground truth map is not known to the agent, we
leverage pre-trained SPTs in an end-to-end framework that has the structure of
mapper and planner built into it which allows seamless generalization to
out-of-distribution maps and goals. SPTs outperform prior state-of-the-art
differentiable planners across all the setups for both manipulation and
navigation tasks, leading to an absolute improvement of 7-19%.

利用数据中的统计规律，我们提出了空间规划变换器 (SPT)，通过学习障碍地图生成长程空间相关的行动来规划移动路径，实现了比先前数据驱动规划方法更好的算法，能够适应不同的障碍地图和任务目标。

可微空间规划：基于 Transformer 的实现

Differentiable Spatial Planning using Transformers

We train embodied neural networks to plan and navigate unseen complex 3D
environments, emphasising real-world deployment. Rather than requiring prior
knowledge of the agent or environment, the planner learns to model the state
transitions and rewards. To avoid the potentially hazardous trial-and-error of
reinforcement learning, we focus on differentiable planners such as Value
Iteration Networks (VIN), which are trained offline from safe expert
demonstrations. Although they work well in small simulations, we address two
major limitations that hinder their deployment. First, we observed that current
differentiable planners struggle to plan long-term in environments with a high
branching complexity. While they should ideally learn to assign low rewards to
obstacles to avoid collisions, we posit that the constraints imposed on the
network are not strong enough to guarantee the network to learn sufficiently
large penalties for every possible collision. We thus impose a structural
constraint on the value iteration, which explicitly learns to model any
impossible actions. Secondly, we extend the model to work with a limited
perspective camera under translation and rotation, which is crucial for real
robot deployment. Many VIN-like planners assume a 360 degrees or overhead view
without rotation. In contrast, our method uses a memory-efficient lattice map
to aggregate CNN embeddings of partial observations, and models the rotational
dynamics explicitly using a 3D state-space grid (translation and rotation). Our
proposals significantly improve semantic navigation and exploration on several
2D and 3D environments, succeeding in settings that are otherwise challenging
for this class of methods. As far as we know, we are the first to successfully
perform differentiable planning on the difficult Active Vision Dataset,
consisting of real images captured from a robot.

本研究提出了一种基于不同可微可导规划器的机器人导航方法，用于规划并导航三维环境中的机器人，并考虑实际应用中的限制，该方法是在无需先验知识的情况下从安全的专家示范中训练产生，能够成功地应用于真实机器人的模拟导航。