6 papers
Don't Mix Rewards, Mix Policies: Policy Decomposition and Optimization for Multi-Reward RL
Ruiming Liang, Yi Zhong, Yizhen Yuan +6
Modern large language models (LLMs) are expected not just to answer correctly, but to adapt their behavior to different human values and use cases. As a result, multi-reward reinfo…
Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving
Yinan Zheng, Tianyi Tan, Bin Huang +11
Diffusion models have become a popular choice for decision-making tasks in robotics, and more recently, are also being considered for solving autonomous driving tasks. However, the…
Dichotomous Diffusion Policy Optimization
Ruiming Liang, Yinan Zheng, Kexin Zheng +9
Diffusion-based policies have gained growing popularity in solving a wide range of decision-making tasks due to their superior expressiveness and controllable generation during inf…
Flow Matching-Based Autonomous Driving Planning with Advanced Interactive Behavior Modeling
Tianyi Tan, Yinan Zheng, Ruiming Liang +6
Modeling interactive driving behaviors in complex scenarios remains a fundamental challenge for autonomous driving planning. Learning-based approaches attempt to address this chall…
Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated Objects via Procedural Generation
Xinyu Lian, Zichao Yu, Ruiming Liang +9
Large-scale articulated objects with high quality are desperately needed for multiple tasks related to embodied AI. Most existing methods for creating articulated objects are eithe…
Diffusion-Based Planning for Autonomous Driving with Flexible Guidance
Yinan Zheng, Ruiming Liang, Kexin Zheng +8
Achieving human-like driving behaviors in complex open-world environments is a critical challenge in autonomous driving. Contemporary learning-based planning approaches such as imi…