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  • Homepage for Jiachen Li
    The ultimate goal of my research is to build trustworthy, interactive, and human-centered autonomous embodied agents that can perceive, understand, and reason about the physical world; safely interact and collaborate with humans; and efficiently coordinate with other intelligent agents so that they can benefit society in daily lives To achieve this goal, I have been pursuing interdisciplinary
  • Jiachen Li - GitHub Pages
    Summary My research vision is to enable reliable, interactive, and human-centered autonomous, embodied agents in open-world environments (e g , mobile manipulators, quadrupeds, autonomous vehicles, drones, humanoids) that can perceive, understand, and reason about the physical world; safely and robustly interact and collaborate with humans; and efficiently coordinate with other agents so they
  • EvolveGraph - GitHub Pages
    Multi-agent interacting systems are prevalent in the world, from pure physical systems to complicated social dynamic systems In many applications, effective understanding of the situation and accurate trajectory prediction of interactive agents play a significant role in downstream tasks, such as decision making and planning In this paper, we propose a generic trajectory forecasting
  • EvolveGraph: Multi-Agent Trajectory Prediction Jiachen Li*, Fan Yang*,
    Accurate multi-agent trajectory prediction is critical in many real-world applications, where a group of entities interact with each other, giving rise to complicated behavior patterns of both individuals and the whole multi-agent system In this work, we address the problem of 1) extracting the underlying interaction patterns with a latent graph structure, which can handle different types of
  • RAIN - GitHub Pages
    RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting Jiachen Li 1,2, Fan Yang 3, Hengbo Ma 2, Srikanth Malla 1, Masayoshi Tomizuka 2, Chiho Choi 1
  • Continual Multi-Agent Interaction Behavior Prediction With Conditional . . .
    Hengbo Ma , Yaofeng Sun, Jiachen Li , Member, IEEE, Masayoshi Tomizuka , Life Fellow, IEEE, and Chiho Choi Abstract—Multi-agent trajectory prediction plays a crucial role in robotics and autonomous driving The current mainstream re-search focuses on how to achieve accurate prediction on one large dataset
  • Multi-Agent Driving Behavior Prediction across Different Scenarios with . . .
    Multi-Agent Driving Behavior Prediction across Different Scenarios with Self-Supervised Domain Knowledge Hengbo Ma*, Yaofeng Sun*, Jiachen Li, Masayoshi Tomizuka, Life Fellow, IEEE Abstract—How to make precise multi-agent trajectory pre-diction is a crucial problem in the context of autonomous driving





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