24 citations · 67 across the 30 of their papers we have counts for
7 papers · 1 filter
CFG-Policy: Unified Diffusion Policy Learning Framework via Classifier-free Guidance
Shutong Ding, Zhaoyang Liu, Yimiao Zhou +3
Diffusion policies are increasingly used in robotic control and manipulation, vision-language-action models, and world-model-based agents. Their ability to represent multimodal act…
FASTER: Fast Adjoint Stochastic Transport for Endpoint Refinement in Reward-Guided Image Editing
Yimiao Zhou, Zejia Zhong, Jingya Wang +1
Reward-guided image editing at test time seeks to improve a specified reward while preserving source content and visual plausibility. Many existing approaches optimize candidates t…
Steering Generative Reinforcement Learning into Stable Robotic Controller
Yixuan Wang, Shutong Ding, Ke Hu +3
Diffusion and flow-based generative policies provide a powerful policy class for reinforcement learning by inducing rich stochastic exploration through iterative action generation.…
Conformal Reliability: A New Evaluation Metric for Conditional Generation
Yachen Gao, Xinwei Sun, Yikai Wang +4
Conditional generative models have recently achieved remarkable success in various applications. However, a suitable metric for evaluating the reliability of these models, which ta…
Sample-Efficient Diffusion-based Reinforcement Learning with Critic Guidance
Shutong Ding, Zejia Zhong, Zhongyi Wang +4
Recent advances in reinforcement learning (RL) have achieved great successes by leveraging the multimodality and exploration capability of diffusion policies. Among these approache…
Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios
Yuting Zeng, Zhiwen Zheng, Jingya Wang +6
Safe and efficient assistive planning for visually impaired scenarios remains challenging, since existing methods struggle with multi-objective optimization, generalization, and in…
Distributional Reinforcement Learning with Diffusion Bridge Critics
Shutong Ding, Yimiao Zhou, Ke Hu +5
Recent advances in diffusion-based reinforcement learning (RL) methods have demonstrated promising results in a wide range of continuous control tasks. However, existing works in t…