5 papers
Inverse Optimal Control with Constraint Relaxation
Rahel Rickenbach, Amon Lahr, Melanie N. Zeilinger
Inverse optimal control (IOC) is a promising paradigm for learning and mimicking optimal control strategies from capable demonstrators, or gaining a deeper understanding of their i…
Finite-Sample-Based Reachability for Safe Control with Gaussian Process Dynamics
Manish Prajapat, Johannes Köhler, Amon Lahr +2
Gaussian Process (GP) regression is shown to be effective for learning unknown dynamics, enabling efficient and safety-aware control strategies across diverse applications. However…
Optimal kernel regression bounds under energy-bounded noise
Amon Lahr, Johannes Köhler, Anna Scampicchio +1
Non-conservative uncertainty bounds are key for both assessing an estimation algorithm's accuracy and in view of downstream tasks, such as its deployment in safety-critical context…
Gaussian processes for dynamics learning in model predictive control
Anna Scampicchio, Elena Arcari, Amon Lahr +1
Due to its state-of-the-art estimation performance complemented by rigorous and non-conservative uncertainty bounds, Gaussian process regression is a popular tool for enhancing dyn…
Towards safe and tractable Gaussian process-based MPC: Efficient sampling within a sequential quadratic programming framework
Manish Prajapat, Amon Lahr, Johannes Köhler +2
Learning uncertain dynamics models using Gaussian process~(GP) regression has been demonstrated to enable high-performance and safety-aware control strategies for challenging real-…