no code implementations • 20 Apr 2024 • Zirui Zang, Ahmad Amine, Rahul Mangharam
We also demonstrated the efficiency of this method by deploying the model on a mobile robot.
no code implementations • 25 Mar 2024 • Tom Kuipers, Renukanandan Tumu, Shuo Yang, Milad Kazemi, Rahul Mangharam, Nicola Paoletti
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.
1 code implementation • 26 Jan 2024 • Shuo Yang, Yu Chen, Xiang Yin, Rahul Mangharam
Our approach is computationally efficient, minimally invasive to any reference controller, and applicable to large-scale systems.
1 code implementation • 12 Dec 2023 • Renukanandan Tumu, Matthew Cleaveland, Rahul Mangharam, George J. Pappas, Lars Lindemann
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.
1 code implementation • 19 Sep 2023 • Luigi Berducci, Shuo Yang, Rahul Mangharam, Radu Grosu
Ensuring safety in dynamic multi-agent systems is challenging due to limited information about the other agents.
no code implementations • 20 Jul 2023 • Jiyue He, Arkady Pertsov, John Bullinga, Rahul Mangharam
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.
no code implementations • 11 Jun 2023 • Jiangwei Wang, Shuo Yang, Ziyan An, Songyang Han, Zhili Zhang, Rahul Mangharam, Meiyi Ma, Fei Miao
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.
no code implementations • 1 Apr 2023 • Shuo Yang, George J. Pappas, Rahul Mangharam, Lars Lindemann
However, these perception maps are not perfect and result in state estimation errors that can lead to unsafe system behavior.
1 code implementation • 1 Mar 2023 • Xiatao Sun, Shuo Yang, Mingyan Zhou, Kunpeng Liu, Rahul Mangharam
In this paper, we propose MEGA-DAgger, a new DAgger variant that is suitable for interactive learning with multiple imperfect experts.
no code implementations • 2 Feb 2023 • Renukanandan Tumu, Lars Lindemann, Truong Nghiem, Rahul Mangharam
Predicting the motion of dynamic agents is a critical task for guaranteeing the safety of autonomous systems.
no code implementations • 23 Nov 2022 • Yu Chen, Shuo Yang, Rahul Mangharam, Xiang Yin
This problem is particularly challenging since future information is involved in the synthesis process.
no code implementations • 20 Sep 2022 • Shuo Yang, Shaoru Chen, Victor M. Preciado, Rahul Mangharam
Learning-based controllers, such as neural network (NN) controllers, can show high empirical performance but lack formal safety guarantees.
1 code implementation • 16 Sep 2022 • Hongrui Zheng, Zhijun Zhuang, Johannes Betz, Rahul Mangharam
Generating competitive strategies and performing continuous motion planning simultaneously in an adversarial setting is a challenging problem.
no code implementations • 25 Jan 2021 • Alëna Rodionova, Yash Vardhan Pant, Connor Kurtz, Kuk Jang, Houssam Abbas, Rahul Mangharam
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.
no code implementations • 23 Jun 2020 • Alëna Rodionova, Yash Vardhan Pant, Kuk Jang, Houssam Abbas, Rahul Mangharam
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.
1 code implementation • ICML 2020 • Aman Sinha, Matthew O'Kelly, Hongrui Zheng, Rahul Mangharam, John Duchi, Russ Tedrake
Balancing performance and safety is crucial to deploying autonomous vehicles in multi-agent environments.