We investigate the problem of transferring an expert policy from a source robot to multiple different robots. To solve this problem, we propose a method named $Meta$-$Evolve$ that uses continuous robot evolution to efficiently transfer the policy to each target robot through a set of tree-structured evolutionary robot sequences. The robot evolution tree allows the robot evolution paths to be shared, so our approach can significantly outperform naive one-to-one policy transfer. We present a heuristic approach to determine an optimized robot evolution tree. Experiments have shown that our method is able to improve the efficiency of one-to-three transfer of manipulation policy by up to 3.2$\times$ and one-to-six transfer of agile locomotion policy by 2.4$\times$ in terms of simulation cost over the baseline of launching multiple independent one-to-one policy transfers.

我们研究了从一个源机器人向多个不同机器人传递专家策略的问题。为了解决这个问题，我们提出了一种名为 Meta-Evolve 的方法，通过一组树结构的进化机器人序列，利用连续的机器人进化将策略有效地传递给每个目标机器人。机器人进化树允许共享机器人进化路径，因此我们的方法在一对一策略传递方面明显优于朴素的方式。我们提出了一种启发式方法来确定优化的机器人进化树。实验证明，我们的方法相对于启动多个独立的一对一策略传递基线，能够使操作策略的一对三传递效率提高了 3.2 倍，使敏捷运动策略的一对六传递效率提高了 2.4 倍，从而减少了仿真成本。

元进化：用于一对多策略转移的连续机器人进化