9 papers
MoCaP: Mobile Code-as-Policies for Zero-Shot Mobile Manipulation from Passive Human Videos
Yiyang Cai, Tatsuya Kamijo, Ninghao Lu +2
Passive human videos of everyday activity offer robots access to a massive amount of human experience without requiring a demonstration for each task. However, using this experienc…
Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence
Shuailei Ma, Jiaqi Liao, Xinyang Wang +24
Despite the recent promise in robot control, video generative models suffer from a domain mismatch due to their primary focus on content creation. For example, their design inheren…
DCD-PFN: A Decoupling-Aware Foundation Model for Causal Discovery
Zhengkang Guan, Yikang Chen, Yi He +5
Causal discovery is critical for understanding complex data-generating mechanisms, yet traditional algorithms often struggle with highly non-linear and noisy systems, or suffer fro…
CDLM: Causal Concept-Guided Diffusion Large Language Models
Kairong Han, Nuanqiao Shan, Ziyu Zhao +6
Autoregressive (AR) language models and Diffusion Language Models (DLMs) constitute the two principal paradigms of large language models. However, both paradigms suffer from insuff…
Noise Projection: Closing the Prompt-Agnostic Gap Behind Text-to-Image Misalignment in Diffusion Models
Yunze Tong, Didi Zhu, Zijing Hu +2
In text-to-image generation, different initial noises induce distinct denoising paths with a pretrained Stable Diffusion (SD) model. While this pattern could output diverse images,…
Asynchronous Denoising Diffusion Models for Aligning Text-to-Image Generation
Zijing Hu, Yunze Tong, Fengda Zhang +3
Diffusion models have achieved impressive results in generating high-quality images. Yet, they often struggle to faithfully align the generated images with the input prompts. This…
D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples
Zijing Hu, Fengda Zhang, Kun Kuang
The practical applications of diffusion models have been limited by the misalignment between generated images and corresponding text prompts. Recent studies have introduced direct…
Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards
Zijing Hu, Fengda Zhang, Long Chen +6
Diffusion models have achieved remarkable success in text-to-image generation. However, their practical applications are hindered by the misalignment between generated images and c…
Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments
Haritheja Etukuru, Norihito Naka, Zijin Hu +7
Robot models, particularly those trained with large amounts of data, have recently shown a plethora of real-world manipulation and navigation capabilities. Several independent effo…