Recover: A Neuro-Symbolic Framework for Failure Detection and Recovery

With the increasing use of robots in tasks involving humans in the perception-action loop, understanding the reasons behind failures in both planning and execution is a significant challenge for enhancing the reliability, adaptability, and safety of autonomous systems. Robots need to comprehend why and when failures occur and devise appropriate solutions based on the current situation. To achieve this, robots should be equipped with robust planning, perception, and reasoning capabilities enabling them to analyze failures and propose recovery strategies in real time. The standard approaches to autonomous robots are typically model-based or policy-based [3]. Model-based approaches can involve offline planning, where the robot considers the current state and utilizes its model to predict the next state and potential rewards, enabling it to plan a sequence of actions expected to maximize reward. In online model-based planning instead, the robot continuously re-plans based on the current state, adjusting its actions in response to changes in the environment. Policy-based approaches usually entail either open-loop policy, where the robot predicts a sequence of actions based on the initial state and goal, or closed-loop policy, where the robot predicts individual actions at each moment based on the current state and goal.