This is "Sequential Dexterity", a neural network that controls a robot arm to build legos given a manual \ud83e\udd16

To do this task, the robot needs to chain together multiple skills (grasping, re-orienting, pushing, etc.) and execute without compounding error.

I find some very simple but effective insights in the paper:

- Skill chaining over a long sequence is hard. We need a way to "smooth it over".
- Forward smoothing: train skill [i] to good performance, run it many times to generate possible "end states", and sample from these states to provide initialization for skill [i+1]. Then start training skill [i+1], and so on.
- The problem is that skill [i] is unaware of [i+1], so it may produce bad end states that make life hard for [i+1].
- Backward smoothing: identify what are the good starting states for skill [i+1], then finetune the previous skill [i] towards those states by reinforcement learning.

Website: https://t.co/iyW6jGzyTf
Authors: Yuanpei Chen*, Chen Wang*, Li Fei-Fei, C. Karen Liu
Paper: "Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation" https://t.co/Lx2LvuJs2A