no code implementations • 31 Oct 2023 • Sleiman Safaoui, Abraham P. Vinod, Ankush Chakrabarty, Rien Quirynen, Nobuyuki Yoshikawa, Stefano Di Cairano
For this problem, we present a tractable motion planner that builds upon the strengths of reinforcement learning and constrained-control-based trajectory planning.
no code implementations • 8 Apr 2022 • Sleiman Safaoui, Lars Lindemann, Iman Shames, Tyler H. Summers
Our control approach relies on reformulating these risk predicates as deterministic predicates over mean and covariance states of the system.
1 code implementation • 5 Jan 2022 • Venkatraman Renganathan, Sleiman Safaoui, Aadi Kothari, Benjamin Gravell, Iman Shames, Tyler Summers
Robust autonomy stacks require tight integration of perception, motion planning, and control layers, but these layers often inadequately incorporate inherent perception and prediction uncertainties, either ignoring them altogether or making questionable assumptions of Gaussianity.
1 code implementation • 9 Mar 2021 • Sleiman Safaoui, Benjamin J. Gravell, Venkatraman Renganathan, Tyler H. Summers
We propose a two-phase risk-averse architecture for controlling stochastic nonlinear robotic systems.
Robotics