A key challenge towards the goal of multi-part assembly tasks is finding robust sensorimotor control methods in the presence of uncertainty. In contrast to previous works that rely on a priori knowledge on whether two parts match, we aim to learn this through physical interaction. We propose a hierarchical approach that enables a robot to autonomously assemble parts while being uncertain about part types and positions. In particular, our probabilistic approach learns a set of differentiable filters that leverage the tactile sensorimotor trace from failed assembly attempts to update its belief about part position and type. This enables a robot to overcome assembly failure. We demonstrate the effectiveness of our approach on a set of object fitting tasks. The experimental results indicate that our proposed approach achieves higher precision in object position and type estimation, and accomplishes object fitting tasks faster than baselines.
翻译:实现多部分组装任务目标的一个关键挑战是在不确定的情况下找到稳健的感官质控制方法。 与以前依靠先验性知识来判断两部分是否匹配的工程不同, 我们的目标是通过物理互动来了解这一点。 我们建议了一种分级方法,使机器人能够自主组装部件,同时对部分类型和位置感到不确定。 特别是, 我们的概率方法学会了一套不同的过滤器,利用触觉感官质追踪方法来更新对部分位置和类型的看法。 这让机器人能够克服组装失败。 我们展示了我们对一组对象装配任务的方法的有效性。 实验结果显示,我们提出的方法在目标位置和类型估计方面实现了更高的精确度, 并且完成了比基线更快的物体装配任务。