Counterfactual explanations are generated to describe necessary changes for robot success.

Feedback from humans helps fine-tune the robot's training, making the process more efficient.

This method outperforms other techniques, improving robot learning in new environments.

Robots often struggle with objects and spaces not encountered during training.

The framework employs imitation learning to teach robots specific tasks.

By determining important task elements, it generates new synthetic data by altering unimportant visual concepts.

This data augmentation process helps the robot recognize objects irrespective of non-essential features.