We tackle the problem of learning robotic sensorimotor control policies that
can generalize to visually diverse and unseen environments. Achieving broad
generalization typically requires large datasets, which are difficult to obtain
for task-specific interactive processes such as reinforcement learning or
learning from demonstration. However, much of the visual diversity in the world
can be captured through passively collected datasets of images or videos. In
our method, which we refer to as GPLAC (Generalized Policy Learning with
Attentional Classifier), we use both interaction data and weakly labeled image
data to augment the generalization capacity of sensorimotor policies. Our
method combines multitask learning on action selection and an auxiliary binary
classification objective, together with a convolutional neural network
architecture that uses an attentional mechanism to avoid distractors. We show
that pairing interaction data from just a single environment with a diverse
dataset of weakly labeled data results in greatly improved generalization to
unseen environments, and show that this generalization depends on both the
auxiliary objective and the attentional architecture that we propose. We
demonstrate our results in both simulation and on a real robotic manipulator,
and demonstrate substantial improvement over standard convolutional
architectures and domain adaptation methods.

研究了机器人感知运动控制策略的广义学习问题，提出了一种称为 GPLAC 的方法来提高机器人策略的泛化能力，结合多任务学习和辅助二进制分类目标，通过卷积神经网络和注意力机制，将交互数据与弱标记的数据集相结合，大大提高了对未知环境的泛化能力。