4 papers
MAGE: Multi-scale Autoregressive Generation for Offline Reinforcement Learning
Chenxing Lin, Xinhui Gao, Haipeng Zhang +7
Generative models have gained significant traction in offline reinforcement learning (RL) due to their ability to model complex trajectory distributions. However, existing generati…
One Step Is Enough: Dispersive MeanFlow Policy Optimization
Guowei Zou, Haitao Wang, Hejun Wu +3
Real-time robotic control demands fast action generation. However, existing generative policies based on diffusion and flow matching require multi-step sampling, fundamentally limi…
DM1: MeanFlow with Dispersive Regularization for 1-Step Robotic Manipulation
Guowei Zou, Haitao Wang, Hejun Wu +3
The ability to learn multi-modal action distributions is indispensable for robotic manipulation policies to perform precise and robust control. Flow-based generative models have re…
D2PPO: Diffusion Policy Policy Optimization with Dispersive Loss
Guowei Zou, Weibing Li, Hejun Wu +3
Diffusion policies excel at robotic manipulation by naturally modeling multimodal action distributions in high-dimensional spaces. Nevertheless, diffusion policies suffer from diff…