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Found 4 papers, 0 papers with code
Statistical Safety and Robustness Guarantees for Feedback Motion Planning of Unknown Underactuated Stochastic Systems
We present a method for providing statistical guarantees on runtime safety and goal reachability for integrated planning and control of a class of systems with unknown nonlinear stochastic underactuated dynamics.
Complex Terrain Navigation via Model Error Prediction
Robot navigation traditionally relies on building an explicit map that is used to plan collision-free trajectories to a desired target.
High-Speed Robot Navigation using Predicted Occupancy Maps
Safe and high-speed navigation is a key enabling capability for real world deployment of robotic systems.
Planning with Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants
Our method imposes the feedback law existence as a constraint in a sampling-based planner, which returns a feedback policy around a nominal plan ensuring that, if the Lipschitz constant estimate is valid, the true system is safe during plan execution, reaches the goal, and is ultimately invariant in a small set about the goal.
