I like this effort, and I have a few suggestions:
Humanoid robots are much more difficult than non-humanoid ones. There are a lot more joints than in other designs; the balance question demands both more capable components and more advanced controls; as a consequence of the balance and shape questions, a lot of thought needs to go into wrangling weight ratios, which means preferring more expensive materials for lightness, etc.
In terms of modifying your analysis, I think this cashes out as greater material intensity—the calculations here are done by weight of materials, we just need a way to account for the humanoid robot requiring more processing on all of those materials. We could say something like 1500kg of humanoid robot materials take twice as much processing/refinement as 1500kg of car materials (occasionally this will be about the same; for small fractions of the weight it will be 10x the processing, etc).
The humanoid robots are more vulnerable to bottlenecks than cars. Specifically they need more compute and rare earth elements like neodymium, which will be tough because that supply chain is already strained by new datacenters and AI demands.
I propose an alternative to speed as explanation: all previous forms of automation were local. Each factory had to be automated in bespoke fashion one at a time; a person could move from a factory that was automated to any other factory that had not been yet. The automation equipment had to be made some somewhere and then moved to where the automation was happening.
By contrast, AI is global. Every office on earth can be automated at the same time (relative to historical timescales). There’s no bottleneck chain where the automation has to be deployed to one locality, after being assembled in a different locality, from parts made in many different localities. The limitations are network bandwidth and available compute, both of which are shared resource pools and complements besides.