In this work, we aim to tackle the problem of autonomous grading, where a
dozer is required to flatten an uneven area. In addition, we explore methods
for bridging the gap between a simulated environment and real scenarios. We
design both a realistic physical simulation and a scaled real prototype
environment mimicking the real dozer dynamics and sensory information. We
establish heuristics and learning strategies in order to solve the problem.
Through extensive experimentation, we show that although heuristics are capable
of tackling the problem in a clean and noise-free simulated environment, they
fail catastrophically when facing real world scenarios. As the heuristics are
capable of successfully solving the task in the simulated environment, we show
they can be leveraged to guide a learning agent which can generalize and solve
the task both in simulation and in a scaled prototype environment.

本文旨在解决自主评分问题，通过物理仿真和实时原型环境设计启发式和学习策略，解决在仿真环境中的任务，同时探索物理仿真和真实场景之间的鸿沟

走向真正意义上的自主评分

Towards Autonomous Grading In The Real World

In this paper, we present a system to train driving policies from experiences
collected not just from the ego-vehicle, but all vehicles that it observes.
This system uses the behaviors of other agents to create more diverse driving
scenarios without collecting additional data. The main difficulty in learning
from other vehicles is that there is no sensor information. We use a set of
supervisory tasks to learn an intermediate representation that is invariant to
the viewpoint of the controlling vehicle. This not only provides a richer
signal at training time but also allows more complex reasoning during
inference. Learning how all vehicles drive helps predict their behavior at test
time and can avoid collisions. We evaluate this system in closed-loop driving
simulations. Our system outperforms all prior methods on the public CARLA
Leaderboard by a wide margin, improving driving score by 25 and route
completion rate by 24 points. Our method won the 2021 CARLA Autonomous Driving
challenge. Code and data are available at this https URL

本文提出了一种通过其他车辆的行为来训练自动驾驶策略的系统，使用监督任务来学习不受视角限制的中间表示，进而预测其他车辆的行为以避免碰撞，并在 CARLA Leaderboard 上取得了出色的成绩。

从所有车辆中学习

Learning from All Vehicles

Reinforcement learning has steadily improved and outperform human in lots of
traditional games since the resurgence of deep neural network. However, these
success is not easy to be copied to autonomous driving because the state spaces
in real world are extreme complex and action spaces are continuous and fine
control is required. Moreover, the autonomous driving vehicles must also keep
functional safety under the complex environments. To deal with these
challenges, we first adopt the deep deterministic policy gradient (DDPG)
algorithm, which has the capacity to handle complex state and action spaces in
continuous domain. We then choose The Open Racing Car Simulator (TORCS) as our
environment to avoid physical damage. Meanwhile, we select a set of appropriate
sensor information from TORCS and design our own rewarder. In order to fit DDPG
algorithm to TORCS, we design our network architecture for both actor and
critic inside DDPG paradigm. To demonstrate the effectiveness of our model, We
evaluate on different modes in TORCS and show both quantitative and qualitative
results.

本研究旨在利用深度确定性策略梯度算法来应对自主驾驶中复杂的状态和行动空间，我们选择 The Open Racing Car Simulator (TORCS) 来模拟实验环境，并选择适宜的传感器信息设计奖励机制并建立 DPPG 模型，实验结果表明模型在多种场景下均取得了良好的效果。

自主驾驶的深度强化学习

Deep Reinforcement Learning for Autonomous Driving

Unmanned Aerial Vehicles (UAVs) represent a new frontier in a wide range of
monitoring and research applications. To fully leverage their potential, a key
challenge is planning missions for efficient data acquisition in complex
environments. To address this issue, this article introduces a general
Informative Path Planning (IPP) framework for monitoring scenarios using an
aerial robot, focusing on problems in which the value of sensor information is
unevenly distributed in a target area and unknown a priori . The approach is
capable of learning and focusing on regions of interest via adaptation to map
either discrete or continuous variables on the terrain using
variable-resolution data received from probabilistic sensors. During a mission,
the terrain maps built online are used to plan information-rich trajectories in
continuous 3-D space by optimizing initial solutions obtained by a coarse grid
search. Extensive simulations show that our approach is more efficient than
existing methods. We also demonstrate its real-time application on a
photorealistic mapping scenario using a publicly available dataset and
demonstrate a proof of concept for an agricultural monitoring task.

该论文介绍了一种基于无人机的信息路径规划框架，在复杂环境下实现高效数据采集，通过对地形进行离散或连续变量的映射，学习和关注感兴趣区域，并通过优化初值解得到连续 3D 空间中信息丰富的轨迹，证明该方法比现有方法更高效。