In both industrial and service domains, a central benefit of the use of
robots is their ability to quickly and reliably execute repetitive tasks.
However, even relatively simple peg-in-hole tasks are typically subject to
stochastic variations, requiring search motions to find relevant features such
as holes. While search improves robustness, it comes at the cost of increased
runtime: More exhaustive search will maximize the probability of successfully
executing a given task, but will significantly delay any downstream tasks. This
trade-off is typically resolved by human experts according to simple
heuristics, which are rarely optimal. This paper introduces an automatic,
data-driven and heuristic-free approach to optimize robot search strategies. By
training a neural model of the search strategy on a large set of simulated
stochastic environments, conditioning it on few real-world examples and
inverting the model, we can infer search strategies which adapt to the
time-variant characteristics of the underlying probability distributions, while
requiring very few real-world measurements. We evaluate our approach on two
different industrial robots in the context of spiral and probe search for THT
electronics assembly.

本文介绍了一种基于神经网络模型的机器人搜索策略自动优化方法，通过在大量虚拟环境中训练和少量真实环境测试进行反演，能够适应底层概率分布的时间变化特征，同时减少实际测试的数量，并在螺旋和探头搜索 THT 电子组件装配的两个工业机器人上进行了评估。