We propose a novel knowledge distillation framework for effectively teaching
a sensorimotor student agent to drive from the supervision of a privileged
teacher agent. Current distillation for sensorimotor agents methods tend to
result in suboptimal learned driving behavior by the student, which we
hypothesize is due to inherent differences between the input, modeling
capacity, and optimization processes of the two agents. We develop a novel
distillation scheme that can address these limitations and close the gap
between the sensorimotor agent and its privileged teacher. Our key insight is
to design a student which learns to align their input features with the
teacher's privileged Bird's Eye View (BEV) space. The student then can benefit
from direct supervision by the teacher over the internal representation
learning. To scaffold the difficult sensorimotor learning task, the student
model is optimized via a student-paced coaching mechanism with various
auxiliary supervision. We further propose a high-capacity imitation learned
privileged agent that surpasses prior privileged agents in CARLA and ensures
the student learns safe driving behavior. Our proposed sensorimotor agent
results in a robust image-based behavior cloning agent in CARLA, improving over
current models by over 20.6% in driving score without requiring LiDAR,
historical observations, ensemble of models, on-policy data aggregation or
reinforcement learning.

通过知识蒸馏和模仿学习，教授一个机载学生代理人在 CARLA 中学会驾驶

教练可教学生

Coaching a Teachable Student

Vision-based urban driving is hard. The autonomous system needs to learn to
perceive the world and act in it. We show that this challenging learning
problem can be simplified by decomposing it into two stages. We first train an
agent that has access to privileged information. This privileged agent cheats
by observing the ground-truth layout of the environment and the positions of
all traffic participants. In the second stage, the privileged agent acts as a
teacher that trains a purely vision-based sensorimotor agent. The resulting
sensorimotor agent does not have access to any privileged information and does
not cheat. This two-stage training procedure is counter-intuitive at first, but
has a number of important advantages that we analyze and empirically
demonstrate. We use the presented approach to train a vision-based autonomous
driving system that substantially outperforms the state of the art on the CARLA
benchmark and the recent NoCrash benchmark. Our approach achieves, for the
first time, 100% success rate on all tasks in the original CARLA benchmark,
sets a new record on the NoCrash benchmark, and reduces the frequency of
infractions by an order of magnitude compared to the prior state of the art.
For the video that summarizes this work, see this https URL

本文提出了一种基于视觉的自主行驶系统的训练方法，通过两个阶段的训练，先使用有特权的信息训练代理，再使用纯视觉信息训练传感器驱动代理，最终取得了在 CARLA 基准和 NoCrash 基准测试中的优异成绩。