This frontier connects digital intelligence to robots, laboratories and industry. A plausible answer is not enough: the system must act safely under noise, delay and incomplete data.
About this eventA system learns to predict the consequences of actions, but long physical sequences still break down.
A world model can estimate the consequence of an action before executing it, reducing dangerous trial and error in reality. Its core limit is accumulated prediction error: the longer the imagined sequence, the further the simulation can drift from the physical world.
Source: Google DeepMind · Genie 2About this eventFriction, wear, lighting and random disturbances make reality harder than any training simulation.
Simulation provides cheap and safe data but necessarily simplifies contact, sensors, latency and rare failures. A successful policy must therefore tolerate variation and adapt from limited real experience rather than merely perform well inside a digital twin.
Source 1: NVIDIA · Isaac SimSource 2: Google DeepMind · RT-2About this eventA robot must safely collect its own data and transfer skills across bodies and tasks.
About this eventOne system learns an unfamiliar tool from observation and a short instruction without separate retraining.
Primary material behind this dossier: papers, lab publications and official reports.