Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning

Understand

Prediction is an appealing objective for self-supervised learning of behavioral skills, particularly for autonomous robots.

• However, effectively utilizing predictive models for control, especially with raw image inputs, poses a number of major challenges.
• How should the predictions be used? What happens when they are inaccurate? In this paper, we tackle these questions by proposing a method for learning robotic skills from raw image observations, using only autonomously collected experience.
• We show that even an imperfect model can complete complex tasks if it can continuously retry, but this requires the model to not lose track of the objective (e.g., the object of interest).