Jiachen Li

Generative AI for Autonomous Driving: Frontiers and Opportunities

Yuping Wang, Shuo Xing, Can Cui, Renjie Li et autres

Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation, reasoning, planning, and multimodal understanding. This revolutionary force offers the most promising path yet toward solving one of engineering’s grandest challenges: achieving reliable, …

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Datamodel-Based Data Selection for Nonlinear Data-Enabled Predictive Control

Jiachen Li, Shihao Li, Dongmei Chen

Data-Enabled Predictive Control (DeePC) has emerged as a powerful framework for controlling unknown systems directly from input-output data. For nonlinear systems, recent work has proposed selecting relevant subsets of data columns based on geometric proximity to the current operating point. However, such …

Adaptive Trajectory Bundle Method for Roll-to-Roll Manufacturing Systems

Jiachen Li, Shihao Li

Roll-to-roll (R2R) manufacturing requires precise tension and velocity control under operational constraints. Model predictive control demands gradient computation, while sampling-based methods like MPPI struggle with hard constraint satisfaction. This paper presents an adaptive trajectory bundle method that achieves rigorous constraint handling through …

An LLM-Assisted Multi-Agent Control Framework for Roll-to-Roll Manufacturing Systems

Jiachen Li, Shihao Li, Christopher Martin, Zijun Chen et autres

Roll-to-roll manufacturing requires precise tension and velocity control to ensure product quality, yet controller commissioning and adaptation remain time-intensive processes dependent on expert knowledge. This paper presents an LLM-assisted multi-agent framework that automates control system design and adaptation for R2R systems while …

Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm

Hyeon-Jun Kim, Kanghoon Lee, Junho Park, Jiachen Li et autres

Multi-Agent Reinforcement Learning (MARL) has shown promise in solving complex problems involving cooperation and competition among agents, such as an Unmanned Surface Vehicle (USV) swarm used in search and rescue, surveillance, and vessel protection. However, aligning system behavior with user preferences is …

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Research on Intelligent Control System for Industrial Robots Based on Deep Learning

Jiachen Li

In the contemporary landscape where industrial automation is advancing at an accelerated pace, there is an imperative need for the intelligent upgrading of industrial robots. The paper focuses on the deep learning-based intelligent control system of industrial robots, analyzing the application of …

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TrajEvo: Designing Trajectory Prediction Heuristics via LLM-driven Evolution

Zhikai Zhao, Chuanbo Hua, Federico Berto, Kanghoon Lee et autres

Trajectory prediction is a crucial task in modeling human behavior, especially in fields as social robotics and autonomous vehicle navigation. Traditional heuristics based on handcrafted rules often lack accuracy, while recently proposed deep learning approaches suffer from computational cost, lack of explainability, …

STAD: Joint Spatial-Temporal Dimension and Channel Correlation for Time Series Anomaly Detection

Mingle Zhou, Xingli Wang, Delong Han, Jin Wan et autres

Accurately identifying real anomalies and pseudo-anomalies in complex multi-dimensional time series data has been a difficult problem in time series anomaly detection. To solve this problem, this paper proposes a new framework, STAD, that joint temporal and spatial dimensions. This framework guides …

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Dynamic Event-Triggered Sliding Mode Control of Markov Jump Delayed System with Partially Known Transition Probabilities

Jie Lü, Yang Jia, Xiang Cai, J. S. Luo et autres

This paper investigates the dynamic event-triggered (ET) sliding mode control (SMC) of Markov jump delayed systems (MJDSs) with partially known transition probabilities. Firstly, a dynamic ET scheme is introduced for the Markov SMC system, and the effect of time delays is considered. …

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Adaptive Prediction Ensemble: Improving Out-of-Distribution Generalization of Motion Forecasting

Jiachen Li, Sangjae Bae, David Isele

Deep learning-based trajectory prediction models for autonomous driving often struggle with generalization to out-of-distribution (OOD) scenarios, sometimes performing worse than simple rule-based models. To address this limitation, we propose a novel framework, Adaptive Prediction Ensemble (APE), which integrates deep learning and rule-based …

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CoMamba: Real-time Cooperative Perception Unlocked with State Space Models

Jinlong Li, Xinyu Liu, Baolu Li, Runsheng Xu et autres

Cooperative perception systems play a vital role in enhancing the safety and efficiency of vehicular autonomy. Although recent studies have highlighted the efficacy of vehicle-to-everything (V2X) communication techniques in autonomous driving, a significant challenge persists: how to efficiently integrate multiple high-bandwidth features …

Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments

Mansur Arief, Mike Timmerman, Jiachen Li, David Isele et autres

Training intelligent agents to navigate highly interactive environments presents significant challenges. While guided meta reinforcement learning (RL) approach that first trains a guiding policy to train the ego agent has proven effective in improving generalizability across scenarios with various levels of interaction, …