AI system learns to model how fabrics interact by watching videos

In a paper published on the preprint server Arxiv.org, They claim the system can extrapolate to interactions it hasn't seen before, like those involving multiple shirts and pants, enabling it to make long-term predictions. Causal understanding is the basis of counterfactual reasoning, or the imagining of possible alternatives to events that have already happened. For example, in an image containing a pair of balls connected to each other by a spring, counterfactual reasoning would entail predicting the ways the spring affects the balls' interactions. The perception model is trained to extract certain keypoints (areas of interest) from videos, from which the interference module identifies the variables that govern interactions between pairs of keypoints.