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Found 16 papers, 5 papers with code
PoseINN: Realtime Visual-based Pose Regression and Localization with Invertible Neural Networks
We also demonstrated the efficiency of this method by deploying the model on a mobile robot.
Conformal Off-Policy Prediction for Multi-Agent Systems
In this work, we introduce MA-COPP, the first conformal prediction method to solve OPP problems involving multi-agent systems, deriving joint prediction regions for all agents' trajectories when one or more "ego" agents change their policies.
Learning Local Control Barrier Functions for Safety Control of Hybrid Systems
Our approach is computationally efficient, minimally invasive to any reference controller, and applicable to large-scale systems.
Multi-Modal Conformal Prediction Regions by Optimizing Convex Shape Templates
However, little work has gone into finding non-conformity score functions that produce prediction regions that are multi-modal and practical, i. e., that can efficiently be used in engineering applications.
Learning Adaptive Safety for Multi-Agent Systems
Ensuring safety in dynamic multi-agent systems is challenging due to limited information about the other agents.
Individualization of atrial tachycardia models for clinical applications: Performance of fiber-independent model
Given its reasonably good performance and the availability of readily accessible data for model tuning in cardiac ablation procedures, the fiber-independent model could be a promising tool for clinical applications.
Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications
The STL requirements are designed to include both task specifications according to the objective of each agent and safety specifications, and the robustness values of the STL specifications are leveraged to generate rewards.
Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Prediction
However, these perception maps are not perfect and result in state estimation errors that can lead to unsafe system behavior.
MEGA-DAgger: Imitation Learning with Multiple Imperfect Experts
Imitation learning has been widely applied to various autonomous systems thanks to recent development in interactive algorithms that address covariate shift and compounding errors induced by traditional approaches like behavior cloning.
Physics Constrained Motion Prediction with Uncertainty Quantification
Predicting the motion of dynamic agents is a critical task for guaranteeing the safety of autonomous systems.
You Don't Know When I Will Arrive: Unpredictable Controller Synthesis for Temporal Logic Tasks
This problem is particularly challenging since future information is involved in the synthesis process.
Differentiable Safe Controller Design through Control Barrier Functions
Learning-based controllers, such as neural network (NN) controllers, can show high empirical performance but lack formal safety guarantees.
Game-theoretic Objective Space Planning
Generating competitive strategies and performing continuous motion planning simultaneously in an adversarial setting is a challenging problem.
Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme
Urban Air Mobility, the scenario where hundreds of manned and Unmanned Aircraft System (UAS) carry out a wide variety of missions (e. g. moving humans and goods within the city), is gaining acceptance as a transportation solution of the future.
Learning-to-Fly: Learning-based Collision Avoidance for Scalable Urban Air Mobility
With increasing urban population, there is global interest in Urban Air Mobility (UAM), where hundreds of autonomous Unmanned Aircraft Systems (UAS) execute missions in the airspace above cities.
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis
Balancing performance and safety is crucial to deploying autonomous vehicles in multi-agent environments.
