Learning Visual Servoing with Deep Features and Fitted Q-Iteration

Understand

Visual servoing involves choosing actions that move a robot in response to observations from a camera, in order to reach a goal configuration in the world.

• Standard visual servoing approaches typically rely on manually designed features and analytical dynamics models, which limits their generalization capability and often requires extensive application-specific feature and model engineering.
• In this work, we study how learned visual features, learned predictive dynamics models, and reinforcement learning can be combined to learn visual servoing mechanisms.
• We focus on target following, with the goal of designing algorithms that can learn a visual servo using low amounts of data of the target in question, to enable quick adaptation to new targets.