6 papers
Distributionally Robust Planning with Adaptive Control
Astghik Hakobyan, Amaras Nazarians, Aditya Gahlawat +2
Safe operation of autonomous systems requires robustness to both model uncertainty and uncertainty in the environment. We propose DRP-AC, a hierarchical framework fo…
Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomy
Aditya Gahlawat, Ahmed Aboudonia, Sandeep Banik +5
Imitation learning (IL) enables autonomous behavior by learning from expert demonstrations. While more sample-efficient than comparative alternatives like reinforcement learning, I…
-DRAC: Distributionally Robust Adaptive Control
Aditya Gahlawat, Sambhu H. Karumanchi, Naira Hovakimyan
Data-driven machine learning methodologies have attracted considerable attention for the control and estimation of dynamical systems. However, such implementations suffer from a la…
Wasserstein Distributionally Robust Adaptive Covariance Steering
Aditya Gahlawat, Vivek Khatana, Duo Wang +3
We present a methodology for predictable and safe covariance steering control of uncertain nonlinear stochastic processes. The systems under consideration are subject to general un…
Quad: Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees
Zhuohuan Wu, Sheng Cheng, Pan Zhao +6
Quadrotors that can operate predictably in the presence of imperfect model knowledge and external disturbances are crucial in safety-critical applications. We present L1Quad, a con…
Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimation
Pan Zhao, Ziyao Guo, Yikun Cheng +3
This paper presents an approach to trajectory-centric learning control based on contraction metrics and disturbance estimation for nonlinear systems subject to matched uncertaintie…