11 citations · 17 across the 7 of their papers we have counts for
6 papers · 1 filter
Fast and Robust Visuomotor Riemannian Flow Matching Policy
Haoran Ding, Noémie Jaquier, Jan Peters +1
Diffusion-based visuomotor policies excel at learning complex robotic tasks by effectively combining visual data with high-dimensional, multi-modal action distributions. However, d…
MoVEInt: Mixture of Variational Experts for Learning Human-Robot Interactions from Demonstrations
Vignesh Prasad, Alap Kshirsagar, Dorothea Koert +3
Shared dynamics models are important for capturing the complexity and variability inherent in Human-Robot Interaction (HRI). Therefore, learning such shared dynamics models can enh…
Transition State Clustering for Interaction Segmentation and Learning
Fabian Hahne, Vignesh Prasad, Alap Kshirsagar +4
Hidden Markov Models with an underlying Mixture of Gaussian structure have proven effective in learning Human-Robot Interactions from demonstrations for various interactive tasks v…
Kinematically Constrained Human-like Bimanual Robot-to-Human Handovers
Yasemin Göksu, Antonio De Almeida Correia, Vignesh Prasad +4
Bimanual handovers are crucial for transferring large, deformable or delicate objects. This paper proposes a framework for generating kinematically constrained human-like bimanual…
Evetac: An Event-based Optical Tactile Sensor for Robotic Manipulation
Niklas Funk, Erik Helmut, Georgia Chalvatzaki +2
Optical tactile sensors have recently become popular. They provide high spatial resolution, but struggle to offer fine temporal resolutions. To overcome this shortcoming, we study…
Learning Multimodal Latent Dynamics for Human-Robot Interaction
Vignesh Prasad, Lea Heitlinger, Dorothea Koert +3
This article presents a method for learning well-coordinated Human-Robot Interaction (HRI) from Human-Human Interactions (HHI). We devise a hybrid approach using Hidden Markov Mode…