I am a PhD candidate in Mechanical Engineering at the California Institute of Technology (Caltech) and part of the AMBER Lab, advised by Dr. Aaron D. Ames. My research is focussed on nonlinear control applied to robotic systems.
Prior to coming to Caltech, I obtained my BS in Civil Engineering (Systems) at the University of California, Berkeley (UC Berkeley). While there, I was part of the Berkeley Artificial Intelligence Research (BAIR) Lab and worked with Dr. Alexandre M. Bayen on reinforcement learning and optimal control applied to mixed-autonomy traffic using PDE conservation laws and FLOW - I was a member of the CIRCLES project.
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Violation-Free Inter-Sampling Safety: from Control Barrier Functions to Tunable Controllers with Input-to-State Safety Guarantees
Gilbert Bahati, Pio Ong, Aaron D. Ames
(In Review). 2023
We examine the assumption that high sampling frequency leads to minor safety violations for controllers deployed on digital platforms (i.e., zero-order hold implementations). We propose an alternative approach to maintaining safety of such systems by modulating the sampled control input to ensure a more robust safety condition - avoiding any violations.
Stability and Safety through Event-Triggered Intermittent Control with Application to Spacecraft Orbit Stabilization
Pio Ong, Gilbert Bahati, Aaron D. Ames
61th IEEE Conference on Decision and Control (CDC). 2022
We generalize the event-triggered control concept to include state triggers where the controller can be turned off - intermittent control. Using certificate functions (Lyapunov or Barrier Functions), we show that our design of intermittent trigger laws guarantee stability or safety.
Work was done in collaboration with NASA-JPL.
Multi-Adversarial Safety Analysis for Autonomous Vehicles
Gilbert Bahati, Marsalis Gibson, Alexandre M. Bayen
Robotics Science and Systems (RSS): Robust Autonomy workshop. 2020
[pdf] / [video] / [HJ Reachability toolbox]
We study the reduction of conservativeness in Hamilton-Jacobi safety analysis by examining trade-offs between different modeling strategies.
We demonstrate how by introducing structure in the interactions between autonomous vehicles and surrounding vehicles in dense driving scenarios, we are able to uncover safe strategies.
Work was done under the CIRCLES project.