3 accepted papers
Robots must satisfy safety-critical state and input constraints despite disturbances and model mismatch. We introduce a robust model predictive control (RMPC) formulation that is scalable and compatible with real-time implementation. Our formulation guarantees robust constraint satisfaction, input-t…
We propose a system-level synthesis (SLS) framework for robust dynamic games with nonlinear dynamics corrupted by state-dependent additive noise, and nonlinear agent-specific and shared constraints. Each agent designs a nominal trajectory and a causal affine error feedback law to minimize their own …
We study nonlinear output-feedback control from high-resolution RGB images and provide robust constraint satisfaction guarantees despite partial observability, sensor noise, and nonlinear dynamics. To enable scalability while retaining guarantees, we propose: (i) a learned low-dimensional observatio…