Zobrazeno 1 - 10
of 340
pro vyhledávání: '"Li, Jingqi"'
Autor:
Liu, Xinjie, Li, Jingqi, Fotiadis, Filippos, Karabag, Mustafa O., Milzman, Jesse, Fridovich-Keil, David, Topcu, Ufuk
Common feedback strategies in multi-agent dynamic games require all players' state information to compute control strategies. However, in real-world scenarios, sensing and communication limitations between agents make full state feedback expensive or
Externí odkaz:
http://arxiv.org/abs/2410.16441
Reach-Avoid-Stay (RAS) optimal control enables systems such as robots and air taxis to reach their targets, avoid obstacles, and stay near the target. However, current methods for RAS often struggle with handling complex, dynamic environments and sca
Externí odkaz:
http://arxiv.org/abs/2410.02898
Dynamic games offer a versatile framework for modeling the evolving interactions of strategic agents, whose steady-state behavior can be captured by the Nash equilibria of the games. Nash equilibria are often computed in feedback, with policies depen
Externí odkaz:
http://arxiv.org/abs/2409.11257
We propose a new reachability learning framework for high-dimensional nonlinear systems, focusing on reach-avoid problems. These problems require computing the reach-avoid set, which ensures that all its elements can safely reach a target set despite
Externí odkaz:
http://arxiv.org/abs/2408.07866
Autonomous agents should be able to coordinate with other agents without knowing their intents ahead of time. While prior work has studied how agents can gather information about the intent of others, in this work, we study the inverse problem: how a
Externí odkaz:
http://arxiv.org/abs/2402.10182
Solving feedback Stackelberg games with nonlinear dynamics and coupled constraints, a common scenario in practice, presents significant challenges. This work introduces an efficient method for computing approximate local feedback Stackelberg equilibr
Externí odkaz:
http://arxiv.org/abs/2401.15745
Autor:
Papadimitriou, Dimitris, Li, Jingqi
Inferring unknown constraints is a challenging and crucial problem in many robotics applications. When only expert demonstrations are available, it becomes essential to infer the unknown domain constraints to deploy additional agents effectively. In
Externí odkaz:
http://arxiv.org/abs/2304.03367
Autor:
Li, Jingqi, Chiu, Chih-Yuan, Peters, Lasse, Palafox, Fernando, Karabag, Mustafa, Alonso-Mora, Javier, Sojoudi, Somayeh, Tomlin, Claire, Fridovich-Keil, David
Decision-making in multi-player games can be extremely challenging, particularly under uncertainty. In this work, we propose a new sample-based approximation to a class of stochastic, general-sum, pure Nash games, where each player has an expected-va
Externí odkaz:
http://arxiv.org/abs/2304.01945
Autor:
Li, Jingqi, Chiu, Chih-Yuan, Peters, Lasse, Sojoudi, Somayeh, Tomlin, Claire, Fridovich-Keil, David
In multi-agent dynamic games, the Nash equilibrium state trajectory of each agent is determined by its cost function and the information pattern of the game. However, the cost and trajectory of each agent may be unavailable to the other agents. Prior
Externí odkaz:
http://arxiv.org/abs/2301.01398
Publikováno v:
IEEE ICASSP 2023
As a unique biometric that can be perceived at a distance, gait has broad applications in person authentication, social security, and so on. Existing gait recognition methods suffer from changes in viewpoint and clothing and barely consider extractin
Externí odkaz:
http://arxiv.org/abs/2210.11817