Our framework enables the development of the first fully data-driven solutions to active touch on top of learned models for object understanding.

Decision trees have gained popularity as interpretable surrogate models for learning-based control policies.

The increasing scale of vision transformers (ViT) has made the efficient fine-tuning of these large models for specific needs a significant challenge in various applications.