12 citations · 21 across the 8 of their papers we have counts for
10 papers
Self-Improving Video-to-Action Models
Hyunwoo Ryu, Eric Ming Chen, Max Wilcoxson +1
Video generative models are an emerging tool for embodied agents: they can serve as scalable visual planners, generating videos that depict how to complete previously unseen tasks.…
Visual Cue Guided Video Planning for Generalizable Robot Navigation
Hojin Lee, Sizhe Lester Li, Maximilian Hilger +4
Generative video models can serve as a promising backbone for robot navigation by predicting future observations as video plans. Recent approaches often condition video planning on…
Large Video Planner Enables Generalizable Robot Control
Boyuan Chen, Tianyuan Zhang, Haoran Geng +9
General-purpose robots require decision-making models that generalize across diverse tasks and environments. Recent works build robot foundation models by extending multimodal larg…
Generative View Stitching
Chonghyuk Song, Michal Stary, Boyuan Chen +2
Autoregressive video diffusion models are capable of long rollouts that are stable and consistent with history, but they are unable to guide the current generation with conditionin…
Selective Underfitting in Diffusion Models
Kiwhan Song, Jaeyeon Kim, Sitan Chen +3
Diffusion models have emerged as the principal paradigm for generative modeling across various domains. During training, they learn the score function, which in turn is used to gen…
History-Guided Video Diffusion
Kiwhan Song, Boyuan Chen, Max Simchowitz +3
Classifier-free guidance (CFG) is a key technique for improving conditional generation in diffusion models, enabling more accurate control while enhancing sample quality. It is nat…
Controlling diverse robots by inferring Jacobian fields with deep networks
Sizhe Lester Li, Annan Zhang, Boyuan Chen +4
Mirroring the complex structures and diverse functions of natural organisms is a long-standing challenge in robotics. Modern fabrication techniques have greatly expanded the feasib…
Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
Boyuan Chen, Diego Marti Monso, Yilun Du +3
This paper presents Diffusion Forcing, a new training paradigm where a diffusion model is trained to denoise a set of tokens with independent per-token noise levels. We apply Diffu…
DittoGym: Learning to Control Soft Shape-Shifting Robots
Suning Huang, Boyuan Chen, Huazhe Xu +1
Robot co-design, where the morphology of a robot is optimized jointly with a learned policy to solve a specific task, is an emerging area of research. It holds particular promise f…
Kubric: A scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer +32
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and trainin…