Ever wonder why sometimes struggle to manipulate objects that humans can pick up with ease? Manipulation tasks need to be abstracted into feature representations before machines can use them to learn policies (i.e., skills), and these representations usually need to be manually predefined — a challenging undertaking in complex tasks involving deformable objects, for instance, or varying material properties.

A viable alternative is deep learning methods, which provide a means for robots to acquire representations autonomously from experience. Toward that end, researchers at Carnegie Mellon University describe in a preprint paper (““) a method for combining prior task knowledge and experience-based learning to acquire representations, focusing on the task of cutting cucumbers and tomatoes into slices.

“Learning to slice vegetables is a complex task, as

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