5 papers
Two-Steps Diffusion Policy for Robotic Manipulation via Genetic Denoising
Mateo Clemente, Leo Brunswic, Rui Heng Yang +5
Diffusion models, such as diffusion policy, have achieved state-of-the-art results in robotic manipulation by imitating expert demonstrations. While diffusion models were originall…
A Theory of Multi-Agent Generative Flow Networks
Leo Maxime Brunswic, Haozhi Wang, Shuang Luo +3
Generative flow networks utilize a flow-matching loss to learn a stochastic policy for generating objects from a sequence of actions, such that the probability of generating a patt…
RA-DP: Rapid Adaptive Diffusion Policy for Training-Free High-frequency Robotics Replanning
Xi Ye, Rui Heng Yang, Jun Jin +2
Diffusion models exhibit impressive scalability in robotic task learning, yet they struggle to adapt to novel, highly dynamic environments. This limitation primarily stems from the…
Ergodic Generative Flows
Leo Maxime Brunswic, Mateo Clemente, Rui Heng Yang +3
Generative Flow Networks (GFNs) were initially introduced on directed acyclic graphs to sample from an unnormalized distribution density. Recent works have extended the theoretical…
HIPPo: Harnessing Image-to-3D Priors for Model-free Zero-shot 6D Pose Estimation
Yibo Liu, Zhaodong Jiang, Binbin Xu +7
This work focuses on model-free zero-shot 6D object pose estimation for robotics applications. While existing methods can estimate the precise 6D pose of objects, they heavily rely…