Why humanoid robot demos still fail the generalization test
Janine Sinanan-Singh, Director of Gen AI Research at Appen, explains why humanoid robot demos often fail the generalization test, emphasizing that high-quality egocentric video data and human judgment are critical for training models that can handle out-of-distribution tasks. She discusses the shift from classical robotics to AI-driven approaches, noting that while foundation models like Gemini offer zero-shot capabilities, latency and the need for intent understanding remain significant hurdles. The conversation also covers the role of synthetic data in scaling training, the importance of multi-agent systems for safety, and the economic pressures driving token optimization in AI development.
