Backpropagation through signal temporal logic specifications: Infusing logical structure into gradient-based methods
Abstract
This paper presents a technique, named STLCG, to compute the quantitative semantics of Signal Temporal Logic (STL) formulas using computation graphs. STLCG provides a platform which enables the incorporation of logical specifications into robotics problems that benefit from gradient-based solutions. Specifically, STL is a powerful and expressive formal language that can specify spatial and temporal properties of signals generated by both continuous and hybrid systems. The quantitative semantics of STL provide a robustness metric, that is, how much a signal satisfies or violates an STL specification. In this work, we devise a systematic methodology for translating STL robustness formulas into computation graphs. With this representation, and by leveraging off-the-shelf automatic differentiation tools, we are able to efficiently backpropagate through STL robustness formulas and hence enable a natural and easy-to-use integration of STL specifications with many gradient-based approaches used in robotics. Through a number of examples stemming from various robotics applications, we demonstrate that STLCG is versatile, computationally efficient, and capable of incorporating human-domain knowledge into the problem formulation.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 28, 2022
- Source ID
- 10.1177/02783649221082115
Entities
People
- Karen Leung
- Marco Pavone
- Nikos Aréchiga
Organizations
- Office of Naval Research
- Stanford University
- Toyota Research Institute