Adapting Deep Visuomotor Representations with Weak Pairwise Constraints

Understand

Real-world robotics problems often occur in domains that differ significantly from the robot's prior training environment.

• For many robotic control tasks, real world experience is expensive to obtain, but data is easy to collect in either an instrumented environment or in simulation.
• We propose a novel domain adaptation approach for robot perception that adapts visual representations learned on a large easy-to-obtain source dataset (e.g.
• synthetic images) to a target real-world domain, without requiring expensive manual data annotation of real world data before policy search.