  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.
