Deep Reinforcement Learning (DRL) has exhibited efficacy in resolving the
Local Path Planning (LPP) problem. However, such application in the real world
is immensely limited due to the deficient efficiency and generalization
capability of DRL. To alleviate these two issues, a solution named Color is
proposed, which consists of an Actor-Sharer-Learner (ASL) training framework
and a mobile robot-oriented simulator Sparrow. Specifically, the ASL framework,
intending to improve the efficiency of the DRL algorithm, employs a Vectorized
Data Collection (VDC) mode to expedite data acquisition, decouples the data
collection from model optimization by multithreading, and partially connects
the two procedures by harnessing a Time Feedback Mechanism (TFM) to evade data
underuse or overuse. Meanwhile, the Sparrow simulator utilizes a 2D grid-based
world, simplified kinematics, and conversion-free data flow to achieve a
lightweight design. The lightness facilitates vectorized diversity, allowing
diversified simulation setups across extensive copies of the vectorized
environments, resulting in a notable enhancement in the generalization
capability of the DRL algorithm being trained. Comprehensive experiments,
comprising 57 benchmark video games, 32 simulated and 36 real-world LPP
scenarios, have been conducted to corroborate the superiority of our method in
terms of efficiency and generalization. The code and the video of the
experiments can be accessed on our website.

提出了名为 Color 的解决方案，利用 Actor-Sharer-Learner（ASL）训练框架和针对移动机器人的模拟器 Sparrow 来为 Deep Reinforcement Learning（DRL）算法提高效率和泛化能力，综合实验表明该方法在效率和泛化能力方面具有优越性。

通过部分解耦强化学习和矢量化多样性，在一个小时内训练现实世界的本地路径规划器

Train a Real-world Local Path Planner in One Hour via Partially  Decoupled Reinforcement Learning and Vectorized Diversity

This paper presents a novel obstacle avoidance system for road robots
equipped with RGB-D sensor that captures scenes of its way forward. The purpose
of the system is to have road robots move around autonomously and constantly
without any collision even with small obstacles, which are often missed by
existing solutions. For each input RGB-D image, the system uses a new two-stage
semantic segmentation network followed by the morphological processing to
generate the accurate semantic map containing road and obstacles. Based on the
map, the local path planning is applied to avoid possible collision.
Additionally, optical flow supervision and motion blurring augmented training
scheme is applied to improve temporal consistency between adjacent frames and
overcome the disturbance caused by camera shake. Various experiments are
conducted to show that the proposed architecture obtains high performance both
in indoor and outdoor scenarios.

本文提出了一种基于 RGB-D 传感器的道路机器人避障系统，使用两阶段语义分割网络生成语义地图以便进行局部路径规划，采用光流监督和运动模糊增强的训练方法，实现室内外道路机器人的无碰撞自主导航。