Transformer-based AI presents both opportunities and fresh problems. Transfer learning — the ability to take a model trained on one kind of robot and deploy it on another — has been a long-standing challenge, as even small changes in environment or robot configuration can throw models off.⁴³Transformers can train on data from multiple robots to create more general policies that work across varied embodiments and environments.⁴⁴ However, training one of these models requires colossal amounts of data.

One potential workaround involves training models in simulations before porting them over to real-world robots.⁴⁵ However, many of the tasks planned for robots, such as handling soft and delicate objects, remain hard to simulate.⁴⁶ Designing virtual worlds is complicated and expensive, requiring sustained effort from multidisciplinary teams.⁴⁷ But high-fidelity simulators are becoming increasingly accessible,⁴⁸,⁴⁹ and there have been significant advances in techniques for transferring skills learned in virtual environments to real-world robots.⁵⁰ Emerging “world models” that can generate physically realistic 3D environments from scratch could also become a powerful tool for training robots.⁵¹

Pooling data-collection efforts will be crucial going forward, and there are promising efforts to create massive, open, robotics datasets.⁵²,⁵³ There will also be a growing focus on more data-efficient training approaches.⁵⁴,⁵⁵ Data shortages are likely to ease as more robots are deployed, though this will benefit the private companies that build them more than academic researchers.