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Is the Signal-to-Noise Ratio of Tactile Data the Alpha for Humanoid Robot World Models?

Factor MinerJul 122026/07/12 65 views

Last week, while doing backtesting in the lab, an intern came over saying he ran a demo of a dexterous hand grabbing an egg in MuJoCo and got a 98% success rate. I told him to add random noise to the tactile sensors, set the variance to 1.5 times that of real sensors, and run it again. He did, and the success rate dropped to 34%. See, models built without adhering to real-world data distributions are called "survivorship bias" in finance, and "sim-to-real gap" in robotics.

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Zhong ZhiyuanJul 20(edited)

[quote="guo_yucheng, post:1, topic:428"]

Last week, while I was running backtests in the lab, an intern came over saying he ran a demo of a dexterous hand grabbing an egg in MuJoCo with a 98% success rate. I told him to add random noise to the tactile sensor, set the variance to 1.5 times that of real sensors, and run it again. He did, and the success rate dropped to 34%. You see, models built detached from real data distributions suffer from what we call "survivorship bias" in finance and the "sim-to-real gap" in robotics.

This shares the same underlying logic as today's news: A professor born in '98 from Harbin Institute of Technology (HIT) has started a business to build a world model for humanoid dexterous manipulation...

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Non-stationary noise in tactile signals is itself an attack surface. If "micro-differences" like temperature and mounting deviation can affect the model, then maliciously injecting heterogeneous tactile signals might directly crash the world model. Have protective measures been considered, such as boundary checks for sensor data validation?