Name
Title | ||
|---|---|---|
Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environments |
Abstract | ||
|---|---|---|
Control barrier functions (CBFs) have become a popular tool to enforce safety of a control system. CBFs are commonly utilized in a quadratic program formulation (CBF-QP) as safety-critical constraints. A class K function in CBFs usually needs to be tuned manually in order to balance the trade-off between performance and safety for each environment. However, this process is often heuristic and can become intractable for high relative-degree systems. Moreover, it prevents the CBF-QP from generalizing to different environments in the real world. By embedding the optimization procedure of the exponential control barrier function based quadratic program (ECBF-QP) as a differentiable layer within a deep learning architecture, we propose a differentiable safety-critical control framework that enables generalization to new environments for high relative-degree systems with forward invariance guarantees. Finally, we validate the proposed control design with 2D double and quadruple integrator systems in various environments. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.23919/ECC55457.2022.9838179 | 2022 EUROPEAN CONTROL CONFERENCE (ECC) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hengbo Ma | 1 | 4 | 2.11 |
| Bike Zhang | 2 | 1 | 2.08 |
| Masayoshi Tomizuka | 3 | 12 | 7.44 |
| Koushil Sreenath | 4 | 0 | 0.68 |