Publications (8)
Speedup Patch: Learning a Plug-and-Play Policy to Accelerate Embodied Manipulation
Zhichao Wu, Junyin Ye, Zhilong Zhang +6
While current embodied policies exhibit remarkable manipulation skills, their execution remains unsatisfactorily slow as they inherit the tardy pacing of human demonstrations. Exis…
Episodic Return Decomposition by Difference of Implicitly Assigned Sub-Trajectory Reward
Haoxin Lin, Hongqiu Wu, Jiaji Zhang +3
Real-world decision-making problems are usually accompanied by delayed rewards, which affects the sample efficiency of Reinforcement Learning, especially in the extremely delayed c…
Any-step Dynamics Model Improves Future Predictions for Online and Offline Reinforcement Learning
Haoxin Lin, Yu-Yan Xu, Yihao Sun +6
Model-based methods in reinforcement learning offer a promising approach to enhance data efficiency by facilitating policy exploration within a dynamics model. However, accurately…
Model-based Reinforcement Learning with Multi-step Plan Value Estimation
Haoxin Lin, Yihao Sun, Jiaji Zhang +1
A promising way to improve the sample efficiency of reinforcement learning is model-based methods, in which many explorations and evaluations can happen in the learned models to sa…
How Should World Models Be Evaluated for Embodied Decision-Making? A Decision-Making-Centric Position
Yang Yu, Shiyuan Zhang, Yifei Sheng +2
World models have become a central abstraction in modern AI. The term now refers to several different objects: action-conditioned environment models, latent imagination models, fut…
ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller
Haoxin Lin, Junjie Zhou, Daheng Xu +1
Braking system, the key module to ensure the safety and steer-ability of current vehicles, relies on extensive manual calibration during production. Reducing labor and time consump…
Towards Practical World Model-based Reinforcement Learning for Vision-Language-Action Models
Zhilong Zhang, Haoxiang Ren, Yihao Sun +6
Vision-Language-Action (VLA) models show strong generalization for robotic control, but finetuning them with reinforcement learning (RL) is constrained by the high cost and safety…
WHALE: Towards Generalizable and Scalable World Models for Embodied Decision-making
Zhilong Zhang, Ruifeng Chen, Junyin Ye +8
World models play a crucial role in decision-making within embodied environments, enabling cost-free explorations that would otherwise be expensive in the real world. To facilitate…