Continual Learning for Multimodal Data Fusion of a Soft Gripper

Understand

Continual learning (CL) refers to the ability of an algorithm to continuously and incrementally acquire new knowledge from its environment while retaining previously learned information.

• A model trained on one data modality often fails when tested with a different modality.
• A straightforward approach might be to fuse the two modalities by concatenating their features and training the model on the fused data.
• However, this requires retraining the model from scratch each time it encounters a new domain.