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If I understand correctly, there are the novel features of this paper: - Use neural network to learn (state, action) -> (state, prev_state) separately. - Use

If I understand correctly, there are the novel features of this paper: - Use neural network to learn (state, action) -> (state, prev_state) separately. - Use this neural network to predict state in 100-step horizon and use that prediction to chose action in RL setting. This method reduces sample complexity of RL. The method still isn't beating model-free methods with lots of data. I think the interesting part was using micro insects which unfortunately are not available commercially and results may not be reproducible by others.

dekhn 9y ago
If I can read the picture properly, the microinsect is just a small plastic part (injection molded? it doesn't look printed), some servos, and a microcontroller. Any lab that's serious in this area could recreate it. https://robotics.eecs.berkeley.edu/~ronf/PAPERS/dhaldane-icra15.pdf https://robotics.eecs.berkeley.edu/~ronf/PAPERS/dhaldane-icr... Ah, OK. It's a bit more than that. They had to upgrade the bot significantly with next-gen materials to achieve high speeds, https://spectrum.ieee.org/automaton/robotics/robotics-hardware/icra-2015-x2velociroach-smashes-speed-record-for-tiny-legged-robots https://spectrum.ieee.org/automaton/robotics/robotics-hardwa...