李飞飞最新访谈：人也不全靠现实数据学习啊
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TL;DR - Fei-Fei Li's World Labs acquired robotics simulation startup SceniX and, in an a16z interview with co-founder Yunzhu Li, laid out a plan to build a model-agnostic "digital training ground" where robots are trained and evaluated in simulation before real-world deployment. It matters because it frames scarce robot training/eval data — not hardware or policy architecture — as the field's core bottleneck.
- SceniX builds a real-to-sim-to-real pipeline with dense reconstruction (appearance, geometry, dynamics) aligned to real environments; World Labs contributes Marble, a foundation model turning single/multiple images or text into geometrically consistent worlds, plus sparse reconstruction and generative 3D strengths.
- The stated case against pure video-model routes is consistency (spatial, temporal, cross-view, cross-interaction) — video predictors can make pushed objects vanish, giving no usable training signal.
- Simulation is pitched for two gains: reliability via systematic randomization of lighting, geometry, friction and physics parameters to cover the state space, and efficiency, including speeding up robot behavior beyond teleoperation collection rates; evaluation is called the bigger neglected problem, since distinguishing a 90% from a 92% checkpoint in the real world is orders of magnitude slower than LLM eval, plus dangerous and expensive.
- Positioning is deliberately neutral — no hardware, no robot brain; data can train models from scratch or fine-tune VLA/World Action models, on the thesis that "models get replaced, infrastructure doesn't." Near-term focus is semi-structured environments (warehouses, restaurants) over unstructured homes; Li cites Waymo's billions of simulated hours and argues simulation enables counterfactual reasoning humans also perform.
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李飞飞最新访谈：人也不全靠现实数据学习啊
TL;DR - Fei-Fei Li's World Labs acquired robotics simulation startup SceniX and, in an a16z interview with co-founder Yunzhu Li, laid out a plan to build a model-agnostic "digital training ground" where robots are trained and evaluated in simulation before real-world deployment. It matters because it frames scarce robot training/eval data — not hardware or policy architecture — as the field's core bottleneck.
- SceniX builds a real-to-sim-to-real pipeline with dense reconstruction (appearance, geometry, dynamics) aligned to real environments; World Labs contributes Marble, a foundation model turning single/multiple images or text into geometrically consistent worlds, plus sparse reconstruction and generative 3D strengths.
- The stated case against pure video-model routes is consistency (spatial, temporal, cross-view, cross-interaction) — video predictors can make pushed objects vanish, giving no usable training signal.
- Simulation is pitched for two gains: reliability via systematic randomization of lighting, geometry, friction and physics parameters to cover the state space, and efficiency, including speeding up robot behavior beyond teleoperation collection rates; evaluation is called the bigger neglected problem, since distinguishing a 90% from a 92% checkpoint in the real world is orders of magnitude slower than LLM eval, plus dangerous and expensive.
- Positioning is deliberately neutral — no hardware, no robot brain; data can train models from scratch or fine-tune VLA/World Action models, on the thesis that "models get replaced, infrastructure doesn't." Near-term focus is semi-structured environments (warehouses, restaurants) over unstructured homes; Li cites Waymo's billions of simulated hours and argues simulation enables counterfactual reasoning humans also perform.