Zobrazeno 1 - 10
of 1 599
pro vyhledávání: '"He, Jianping"'
This paper develops a direct data-driven inverse optimal control (3DIOC) algorithm for the linear time-invariant (LTI) system who conducts a linear quadratic (LQ) control, where the underlying objective function is learned directly from measured inpu
Externí odkaz:
http://arxiv.org/abs/2409.10884
Humans often learn new skills by imitating the experts and gradually developing their proficiency. In this work, we introduce Stochastic Trajectory Optimization for Demonstration Imitation (STODI), a trajectory optimization framework for robots to im
Externí odkaz:
http://arxiv.org/abs/2408.03131
Large language models (LLMs) have demonstrated remarkable capabilities, but their power comes with significant security considerations. While extensive research has been conducted on the safety of LLMs in chat mode, the security implications of their
Externí odkaz:
http://arxiv.org/abs/2407.17915
Computer vision techniques have empowered underwater robots to effectively undertake a multitude of tasks, including object tracking and path planning. However, underwater optical factors like light refraction and absorption present challenges to und
Externí odkaz:
http://arxiv.org/abs/2406.14973
Designing controllers to generate various trajectories has been studied for years, while recently, recovering an optimal controller from trajectories receives increasing attention. In this paper, we reveal that the inherent linear quadratic regulator
Externí odkaz:
http://arxiv.org/abs/2312.16572
Inverse reinforcement learning (IRL) usually assumes the model of the reward function is pre-specified and estimates the parameter only. However, how to determine a proper reward model is nontrivial. A simplistic model is less likely to contain the r
Externí odkaz:
http://arxiv.org/abs/2312.16566
This paper considers a novel co-design problem of the optimal \textit{sequential} attack, whose attack strategy changes with the time series, and in which the \textit{sequential} attack selection strategy and \textit{sequential} attack signal are sim
Externí odkaz:
http://arxiv.org/abs/2311.09933
Autor:
Xu, Tao, He, Jianping
It is critical and challenging to design robust predictors for stochastic dynamical systems (SDSs) with uncertainty quantification (UQ) in the prediction. Specifically, robustness guarantees the worst-case performance when the predictor's information
Externí odkaz:
http://arxiv.org/abs/2311.07108
Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs). Recently, Large Language Models (LLMs) have shown promise in effectively identifying and cat
Externí odkaz:
http://arxiv.org/abs/2309.16150
The integration of Large Language Models (LLMs) into robotics has revolutionized human-robot interactions and autonomous task planning. However, these systems are often unable to self-correct during the task execution, which hinders their adaptabilit
Externí odkaz:
http://arxiv.org/abs/2309.12089