Apple 首款家用机器人因内部分歧和软件进展缓慢面临再度延期

The key shift is moving agent training from offline imitation of scripted trajectories to online learning inside real apps, which targets error recovery — the point where most SFT-trained UI agents break. Researchers and engineers building device-side automation agents can study how the planning/grounding split trades inference cost against robustness. Compared with routing every step through a frontier VLM, this points toward cheaper on-device navigation stacks.