12 papers
Human-as-Humanoid: Enabling Zero-Shot Humanoid Learning from Ego-Exo Human Videos with Human-Aligned Embodiments
Xiaopeng Lin, Ruoqi Yang, Shijie Lian +14
Vision-language-action (VLA) models across robot embodiments require high-quality observation--action supervision to learn deployable action distributions, yet scaling such robot d…
Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout
Haozhuang Chi, Daosheng Qiu, Hao Su +4
Safe L2/L3 driving automation requires anticipating human-in-the-loop reactions during shared-control transitions. While most driving world models forecast the external environment…
World Engine: Towards the Era of Post-Training for Autonomous Driving
Tianyu Li, Li Chen, Caojun Wang +16
Autonomous vehicles must operate safely in the real world, where errors can have severe consequences. Although modern end-to-end driving policies excel in routine scenarios, their…
Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning
Ziyang Yao, Haochen Liu, Yuncheng Jiang +10
Autonomous driving requires reasoning about how ego actions shape future world evolution, rather than merely mapping observations to actions. However, most end-to-end methods rely…
Zero-Shot UAV Navigation in Forests via Relightable 3D Gaussian Splatting
Zinan Lv, Yeqian Qian, Chen Sang +3
UAV navigation in unstructured outdoor environments using passive monocular vision is hindered by the substantial visual domain gap between simulation and reality. While 3D Gaussia…
SimScale: Learning to Drive via Real-World Simulation at Scale
Haochen Tian, Tianyu Li, Haochen Liu +11
Achieving fully autonomous driving systems requires learning rational decisions in a wide span of scenarios, including safety-critical and out-of-distribution ones. However, such c…
DecompGAIL: Learning Realistic Traffic Behaviors with Decomposed Multi-Agent Generative Adversarial Imitation Learning
Ke Guo, Haochen Liu, Xiaojun Wu +1
Realistic traffic simulation is critical for the development of autonomous driving systems and urban mobility planning, yet existing imitation learning approaches often fail to mod…
Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving
Haochen Liu, Tianyu Li, Haohan Yang +7
End-to-end autonomous driving has emerged as a promising paradigm for directly mapping sensor inputs to planning maneuvers using learning-based modular integrations. However, exist…
iPad: Iterative Proposal-centric End-to-End Autonomous Driving
Ke Guo, Haochen Liu, Xiaojun Wu +2
End-to-end (E2E) autonomous driving systems offer a promising alternative to traditional modular pipelines by reducing information loss and error accumulation, with significant pot…
Integrating Decision-Making Into Differentiable Optimization Guided Learning for End-to-End Planning of Autonomous Vehicles
Wenru Liu, Yongkang Song, Chengzhen Meng +4
We address the decision-making capability within an end-to-end planning framework that focuses on motion prediction, decision-making, and trajectory planning. Specifically, we form…
Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving
Haochen Liu, Li Chen, Yu Qiao +2
Autonomous driving system aims for safe and social-consistent driving through the behavioral integration among interactive agents. However, challenges remain due to multi-agent sce…
Hybrid-Prediction Integrated Planning for Autonomous Driving
Haochen Liu, Zhiyu Huang, Wenhui Huang +3
Autonomous driving systems require the ability to fully understand and predict the surrounding environment to make informed decisions in complex scenarios. Recent advancements in l…