5 papers
RL-Guided PAC-NMPC for Probabilistically-Safe Perception-Based Navigation in Unknown Environments
Adam Polevoy, Dillon Capalongo, Katherine Tang +3
In this paper, we present an approach for combining stochastic nonlinear model predictive control (SNMPC) and reinforcement learning (RL) to enable probabilistically-safe perceptio…
A Multimodal Stochastic Planning Approach for Navigation and Multi-Robot Coordination
Mark Gonzales, Ethan Oh, Joseph Moore
In this paper, we present a receding-horizon, sampling-based planner capable of reasoning over multimodal policy distributions. By using the cross-entropy method to optimize a mult…
Stratified Topological Autonomy for Long-Range Coordination (STALC)
Cora A. Duggan, Adam Goertz, Adam Polevoy +5
In this paper, we present Stratified Topological Autonomy for Long-Range Coordination (STALC), a hierarchical planning approach for multi-robot coordination in real-world environme…
Multi-Agent Feedback Motion Planning using Probably Approximately Correct Nonlinear Model Predictive Control
Mark Gonzales, Adam Polevoy, Marin Kobilarov +1
For many tasks, multi-robot teams often provide greater efficiency, robustness, and resiliency. However, multi-robot collaboration in real-world scenarios poses a number of major c…
Robust Perception-Based Navigation using PAC-NMPC with a Learned Value Function
Adam Polevoy, Mark Gonzales, Marin Kobilarov +1
Nonlinear model predictive control (NMPC) is typically restricted to short, finite horizons to limit the computational burden of online optimization. As a result, global planning f…