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it's hard to escape the 'gimmickry' or narrow purpose in a cost-effective manner to allow the company to survive long enough and reach large-scale deployment. The reason is mixture of hardware and software constraints. You need a range of sensors and equipment (end-effectors, batteries, GPUs), expensive at lower volumes, to extend the robot's physical capabilities (e.g. reach, manipulation, navigation) and enable certain software robot skills. Besides their dependence on hardware, robot skills are not entirely solved nor general enough to work in all environments, that means the company needs to do R&D and data collection, or purchase bought elsewhere. For example, Generative AI models (LLMs, VLAs, world models) are a boon for robotics thanks to knowledge reuse and eased domain adaptation but they're (for now) somewhat unreliable. It's difficult for such embodied GenAI models to be more than technically correct when performing tasks because they lack or ignore knowledge about the physical world needed to ensure risk-free actions and outcomes. For example, asking for a robot to "pour water on that glass" can lead to dropped bottles/glasses or water pouring on a table because the model won't have a clear models of bottle/glass/water ("entities") nor expectations (nothing broken, nothing wet; only what is more or less expected with the act of pouring water conditioned on the most probable areas for representing the of object of interest. Just have a look at 1X's videos, a well-funded humanoid robot startups, and pay attention to object interactions: how those interactions start and end. |
IMHO the biggest obstacle is that AI is still having trouble with object permanence and real world interactions. I'm hopeful this will be solved in the next few years, and I'm sure teams of people are working in it with vast budgets of DARPA dollars and COSTIND Yuan as well as private industry.
Elon could do it if he could just invent a time machine first.