Learning Dexterity | OpenAI
Learning Dexterity | OpenAI
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We’ve trained a human-like robot hand to manipulate physical objects with unprecedented dexterity. Our system, called Dactyl, is trained entirely in simulation and transfers its knowledge to reality
[...]
What surprised us

Tactile sensing is not necessary to manipulate real-world objects. Our robot receives only the locations of the five fingertips along with the position and orientation of the cube. Although the robot hand has touch sensors on its fingertips, we didn’t need to use them. Generally, we found better performance from using a limited set of sensors that could be modeled effectively in the simulator instead of a rich sensor set with values that were hard to model.

What didn’t pan out

We also found to our surprise that a number of commonly employed techniques did not improve our results.

Decreasing reaction time did not improve performance. Conventional wisdom states that reducing the time between actions should improve performance because the changes between states are smaller and therefore easier to predict. Our current time between actions is 80ms, which is smaller than human reaction time of 150-250ms, but significantly larger than neural network computation time of roughly 25ms. Surprisingly, decreasing time between actions to 40ms required additional training time but did not noticeably improve performance in the real world. It’s possible that this rule of thumb is less applicable to neural network models than to the linear models that are in common use today.

Using real data to train our vision policies didn’t make a difference. In early experiments, we used a combination of simulated and real data to improve our models. The real data was gathered from trials of our policy against an object with embedded tracking markers. However, real data has significant disadvantages compared to simulated data. Position information from tracking markers has latency and measurement error. [...] As our methods developed, our simulator-only error improved until it matched our error from using a mixture of simulated and real data. Our final vision models were trained without real data.
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https://blog.openai.com/learning-dexterity/
https://blog.openai.com/learning-dexterity/

Labels: Oleg Zabluda

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