Safe Goal-Directed Autonomy and the Need for Sound Abstractions

The field of sequential decision making (SDM) captures a range of mathematical frameworks geared towards the synthesis of goal-directed behaviors for autonomous systems. Abstract benchmark problems such as the blocks-world do-benchmark problems such as the blocks-world domain have facilitated immense progress in solution algorithms for SDM. there is some evidence that a direct application of SDM algorithms in real-world situations can produce unsafe behaviors. This is particularly apparent in task and motion planning in robotics. We believe that the reliability of today’s SDM algorithms is limited because SDM models, such as the blocks-world domain, are unsound abstractions (those that yield false inferences) of real world situations. This position paper presents the case for a focused research effort towards the study of sound abstractions of models for SDM and algorithms for efficiently computing safe goaldirected behavior using such abstractions.