Coaching a Teachable Student
Jimuyang Zhang Zanming Huang Eshed Ohn-Bar
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.