GPLAC: Generalizing Vision-Based Robotic Skills using Weakly Labeled Images

Understand

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.