7 papers
Physics-Informed Learning of Feedback-Linearizing Representations
Pavlos Kallinikidis, Fengjun Yang, David Snyder +4
Feedback linearization is a powerful tool in nonlinear control, but finding the linearizing coordinate transform remains challenging. Feedback linearizing transforms are governed b…
Flatness-Preserving Residual Learning for Real-Time Tight Quadrotor Formation Flight
Pei-An Hsieh, Fengjun Yang, Nikolai Matni +1
Quadrotors flying in tight formations are severely affected by turbulent aerodynamic interactions, such as downwash, that can cause catastrophic collisions if left unmodeled. To co…
Learning-Based Fault Detection for Legged Robots in Remote Dynamic Environments
Abriana Stewart-Height, Seema Jahagirdar, Nikolai Matni
Operations in hazardous environments put humans, animals, and machines at high risk for physically damaging consequences. In contrast to humans and animals, quadruped robots cannot…
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…
Scalable Distributed Nonlinear Control Under Flatness-Preserving Coupling
Fengjun Yang, Jake Welde, Nikolai Matni
We study distributed control for a network of nonlinear, differentially flat subsystems subject to dynamic coupling. Although differential flatness simplifies planning and control…
Learning Flatness-Preserving Residuals for Pure-Feedback Systems
Fengjun Yang, Jake Welde, Nikolai Matni
We study residual dynamics learning for differentially flat systems, where a nominal model is augmented with a learned correction term from data. A key challenge is that generic re…
Coordinating Planning and Tracking in Layered Control Policies via Actor-Critic Learning
Fengjun Yang, Nikolai Matni
We propose a reinforcement learning (RL)-based algorithm to jointly train (1) a trajectory planner and (2) a tracking controller in a layered control architecture. Our algorithm ar…