SpaTiaL: Monitoring and Planning of Robotic Tasks Using Spatio-Temporal Logic Specifications

Abstract

Many tasks require robots to manipulate objects while satisfying a complex interplay of spatial and temporal constraints. For instance, a table setting robot first needs to place a mug and then fill it with coffee, while satisfying spatial relations such as forks need to placed left of plates. Existing solutions are often not robot-agnostic, not expressive enough to monitor and plan complex tasks, and require users to manually define object relations. We propose the spatio-temporal framework SpaTiaL that unifies the specification, monitoring, and planning of object-centric robotic tasks. SpaTiaL captures diverse spatial relations between objects and temporal task patterns. Our experiments with recorded data, simulations, and robots demonstrate how SpaTiaL provides real-time monitoring and facilitates online planning. SpaTiaL is open source and easily expandable to new object relations and robotic applications.

Energy-Efficient Cyber-Physical Systems: A Model-Based Approach

“…Using formal methods to analyse complex systems carefully has become a trend in robotics. For example, Yang et al suggested a technique called guided and safe reinforcement learning [66], and Pek et al use something called Spatio-Temporal Logic to plan and keep an eye on complex robot tasks [51]. Our work takes a different approach to planning how autonomous systems move.…”

Energy-Efficient Cyber-Physical Systems: A Model-Based Approach

“…Using formal methods to analyse complex systems carefully has become a trend in robotics. For example, Yang et al suggested a technique called guided and safe reinforcement learning [66], and Pek et al use something called Spatio-Temporal Logic to plan and keep an eye on complex robot tasks [51]. Our work takes a different approach to planning how autonomous systems move.…”