5 citations · 8 across the 12 of their papers we have counts for
4 papers · 1 filter
Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask Learning
Moritz Reuss, Jyothish Pari, Pulkit Agrawal +1
Diffusion Policies have become widely used in Imitation Learning, offering several appealing properties, such as generating multimodal and discontinuous behavior. As models are bec…
Scaling Robot Policy Learning via Zero-Shot Labeling with Foundation Models
Nils Blank, Moritz Reuss, Marcel Rühle +4
A central challenge towards developing robots that can relate human language to their perception and actions is the scarcity of natural language annotations in diverse robot datase…
Multimodal Diffusion Transformer: Learning Versatile Behavior from Multimodal Goals
Moritz Reuss, Ömer Erdinç Yağmurlu, Fabian Wenzel +1
This work introduces the Multimodal Diffusion Transformer (MDT), a novel diffusion policy framework, that excels at learning versatile behavior from multimodal goal specifications…
Towards Diverse Behaviors: A Benchmark for Imitation Learning with Human Demonstrations
Xiaogang Jia, Denis Blessing, Xinkai Jiang +4
Imitation learning with human data has demonstrated remarkable success in teaching robots in a wide range of skills. However, the inherent diversity in human behavior leads to the…