Automatically detecting and recovering from failures is an important but challenging problem for autonomous robots. Most of the recent work on learning to plan from demonstrations lacks the ability to detect and recover from errors in the absence of an explicit state representation and/or a (sub-) goal check function. We propose an approach (blending learning with symbolic search) for automated error discovery and recovery, without needing annotated data of failures. Central to our approach is a neuro-symbolic state representation, in the form of dense scene graph, structured based on the objects present within the environment. This enables efficient learning of the transition function and a discriminator that not only identifies failures but also localizes them facilitating fast re-planning via computation of heuristic distance function. We also present an anytime version of our algorithm, where instead of recovering to the last correct state, we search for a sub-goal in the original plan minimizing the total distance to the goal given a re-planning budget. Experiments on a physics simulator with a variety of simulated failures show the effectiveness of our approach compared to existing baselines, both in terms of efficiency as well as accuracy of our recovery mechanism.

翻译：自动检测故障并从中恢复是自主机器人面临的一个重要但具有挑战性的问题。近期大多数基于演示学习规划的工作，在缺乏显式状态表示和/或（子）目标检查函数的情况下，无法检测错误并从中恢复。我们提出了一种方法（将学习与符号搜索相结合），用于自动错误发现与恢复，且无需标注的故障数据。我们方法的核心是一个神经符号状态表示，其形式为密集场景图，基于环境中存在的对象进行结构化构建。这使得能够高效学习转移函数和一个判别器，该判别器不仅能识别故障，还能定位故障，从而通过计算启发式距离函数促进快速重新规划。我们还提出了我们算法的随时版本，在该版本中，我们不是恢复到最后一个正确状态，而是在原始计划中搜索一个子目标，在给定的重新规划预算下最小化到目标的总距离。在物理模拟器上进行的各种模拟故障实验表明，与现有基线方法相比，我们的方法在恢复机制的效率和准确性方面均表现出有效性。