59 citations · 85 across the 32 of their papers we have counts for
10 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…
TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning
Ge Li, Dong Tian, Hongyi Zhou +3
This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policie…
An Overview of Prototype Formulations for Interpretable Deep Learning
Maximilian Xiling Li, Korbinian Franz Rudolf, Paul Mattes +2
Prototypical part networks offer interpretable alternatives to black-box deep learning models by learning visual prototypes for classification. This work provides a comprehensive a…
Variational Distillation of Diffusion Policies into Mixture of Experts
Hongyi Zhou, Denis Blessing, Ge Li +4
This work introduces Variational Diffusion Distillation (VDD), a novel method that distills denoising diffusion policies into Mixtures of Experts (MoE) through variational inferenc…
MaIL: Improving Imitation Learning with Mamba
Xiaogang Jia, Qian Wang, Atalay Donat +7
This work presents Mamba Imitation Learning (MaIL), a novel imitation learning (IL) architecture that provides an alternative to state-of-the-art (SoTA) Transformer-based policies.…
Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning
Ge Li, Hongyi Zhou, Dominik Roth +4
Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods e…
MP3: Movement Primitive-Based (Re-)Planning Policy
Fabian Otto, Hongyi Zhou, Onur Celik +3
We introduce a novel deep reinforcement learning (RL) approach called Movement Primitive-based Planning Policy (MP3). By integrating movement primitives (MPs) into the deep RL fram…
Goal-Conditioned Imitation Learning using Score-based Diffusion Policies
Moritz Reuss, Maximilian Li, Xiaogang Jia +1
We propose a new policy representation based on score-based diffusion models (SDMs). We apply our new policy representation in the domain of Goal-Conditioned Imitation Learning (GC…
Curriculum-Based Imitation of Versatile Skills
Maximilian Xiling Li, Onur Celik, Philipp Becker +3
Learning skills by imitation is a promising concept for the intuitive teaching of robots. A common way to learn such skills is to learn a parametric model by maximizing the likelih…
Information Maximizing Curriculum: A Curriculum-Based Approach for Imitating Diverse Skills
Denis Blessing, Onur Celik, Xiaogang Jia +4
Imitation learning uses data for training policies to solve complex tasks. However, when the training data is collected from human demonstrators, it often leads to multimodal distr…