This paper presents Latent Sampling-based Motion Planning (L-SBMP), a
methodology towards computing motion plans for complex robotic systems by
learning a plannable latent representation. Recent works in control of robotic
systems have effectively leveraged local, low-dimensional embeddings of
high-dimensional dynamics. In this paper we combine these recent advances with
techniques from sampling-based motion planning (SBMP) in order to design a
methodology capable of planning for high-dimensional robotic systems beyond the
reach of traditional approaches (e.g., humanoids, or even systems where
planning occurs in the visual space). Specifically, the learned latent space is
constructed through an autoencoding network, a dynamics network, and a
collision checking network, which mirror the three main algorithmic primitives
of SBMP, namely state sampling, local steering, and collision checking.
Notably, these networks can be trained through only raw data of the system's
states and actions along with a supervising collision checker. Building upon
these networks, an RRT-based algorithm is used to plan motions directly in the
latent space - we refer to this exploration algorithm as Learned Latent RRT
(L2RRT). This algorithm globally explores the latent space and is capable of
generalizing to new environments. The overall methodology is demonstrated on
two planning problems, namely a visual planning problem, whereby planning
happens in the visual (pixel) space, and a humanoid robot planning problem.

通过学习可规划的潜在表示，本文提出了基于潜在采样的动作规划 (L-SBMP) 的方法，该方法结合了控制机器人系统的最新进展和采样式运动规划 (SBMP) 的技术，可以处理高维度机器人系统的动作规划问题，同时采用了三个算法基本原则，即状态采样，局部转向和碰撞检查。