Search Results for author:
Found 4 papers, 2 papers with code
Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning
For this problem, we present a tractable motion planner that builds upon the strengths of reinforcement learning and constrained-control-based trajectory planning.
Risk-Bounded Temporal Logic Control of Continuous-Time Stochastic Systems
Our control approach relies on reformulating these risk predicates as deterministic predicates over mean and covariance states of the system.
Risk Bounded Nonlinear Robot Motion Planning With Integrated Perception & Control
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.
Risk-Averse RRT* Planning with Nonlinear Steering and Tracking Controllers for Nonlinear Robotic Systems Under Uncertainty
We propose a two-phase risk-averse architecture for controlling stochastic nonlinear robotic systems.
Robotics
