  We are motivated by the goal of generalist robots that can complete a wide
range of tasks across many environments. Critical to this is the robot's
ability to acquire some metric of task success or reward, which is necessary
for reinforcement learning, planning, or knowing when to ask for help. For a
general-purpose robot operating in the real world, this reward function must
also be able to generalize broadly across environments, tasks, and objects,
while depending only on on-board sensor observations (e.g. RGB images). While
deep learning on large and diverse datasets has shown promise as a path towards
such generalization in computer vision and natural language, collecting high
quality datasets of robotic interaction at scale remains an open challenge. In
contrast, "in-the-wild" videos of humans (e.g. YouTube) contain an extensive
collection of people doing interesting tasks across a diverse range of
settings. In this work, we propose a simple approach, Domain-agnostic Video
Discriminator (DVD), that learns multitask reward functions by training a
discriminator to classify whether two videos are performing the same task, and
can generalize by virtue of learning from a small amount of robot data with a
broad dataset of human videos. We find that by leveraging diverse human
datasets, this reward function (a) can generalize zero shot to unseen
environments, (b) generalize zero shot to unseen tasks, and (c) can be combined
with visual model predictive control to solve robotic manipulation tasks on a
real WidowX200 robot in an unseen environment from a single human demo.
