Learning long-horizon tasks such as navigation has presented difficult
challenges for successfully applying reinforcement learning to robotics. From
another perspective, under known environments, sampling-based planning can
robustly find collision-free paths in environments without learning. In this
work, we propose Control Transformer that models return-conditioned sequences
from low-level policies guided by a sampling-based Probabilistic Roadmap (PRM)
planner. We demonstrate that our framework can solve long-horizon navigation
tasks using only local information. We evaluate our approach on
partially-observed maze navigation with MuJoCo robots, including Ant, Point,
and Humanoid. We show that Control Transformer can successfully navigate
through mazes and transfer to unknown environments. Additionally, we apply our
method to a differential drive robot (Turtlebot3) and show zero-shot sim2real
transfer under noisy observations.

本文提出控制变压器 (Control Transformer) 结合基于采样的概率路图规划器 (Probabilistic Roadmap Planner) 的低层策略，应用于机器人领域的长周期任务，结果表明我们的框架可以仅利用局部信息解决长周期导航任务，实现了零 - shot 的模拟现实转移 (transfer)，并在 Ant、Point、Humanoid 和 Turtlebot3 机器人上验证了该方法。