MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction

Author:

Prasad Vignesh1,Koert Dorothea1,Stock-Homburg Ruth2,Peters Jan1,Chalvatzaki Georgia1

Affiliation:

1. Department of Computer Science,TU Darmstadt,Germany

2. Chair for Marketing and Human Resource Management,Department of Law and Economics,TU Darmstadt,Germany

Funder

German Research Foundation (DFG)

Publisher

IEEE

Reference36 articles.

1. Prediction of Human Whole-Body Movements with AE- ProMPs

2. Learning Controllers for Reactive and Proactive Behaviors in Human–Robot Collaboration

3. A recurrent latent variable model for sequential data;chung;Advances in Neural IInformation Processing Systems,2015

4. Stochastic backprop-agation and approximate inference in deep generative models;rezende;International Conference on Machine Learning (ICML),2014

5. Teaching physical collaborative tasks: object-lifting case study with a humanoid

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1. MoVEInt: Mixture of Variational Experts for Learning Human–Robot Interactions From Demonstrations;IEEE Robotics and Automation Letters;2024-07

2. InteRACT: Transformer Models for Human Intent Prediction Conditioned on Robot Actions;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

3. Transition State Clustering for Interaction Segmentation and Learning;Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction;2024-03-11

4. Diffusion Co-Policy for Synergistic Human-Robot Collaborative Tasks;IEEE Robotics and Automation Letters;2024-01

5. Clustering Social Touch Gestures for Human-Robot Interaction;Social Robotics;2023-12-03

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