Hello George,
I’m not sure how much this will help, but here are a few things I might think about too. And these might be things you’re already thinking about too!
First is to be careful with how the data is normalized, think about the domain and range of your inputs and outputs. If all of your circles have a max radius of 10, it might be hard for your model to predict a number larger than that.
Second is maybe you can describe the shapes differently than points. I’m not sure exactly what your data looks like, but coordinates I think might not be important. Without putting the shapes on a coordinate system, you could define any rectangle for example with just a height and width, any circle could be defined with just a radius, and I think a triangle could be defined with two side lengths and the angle between them. You could use these simpler representations as inputs and outputs, and maybe they would be easier for the model to pick up.
Third is maybe try breaking up the problem a bit, like first predicting what type of shape comes next, then predicting the dimensions and size of that shape.
I hope this helps a bit!