David Vogt

About

David Vogt is a robotics researcher whose work centers on human-robot interaction, imitation learning, and machine learning for robotic systems. His most influential contribution is the Intention-Driven Dynamics Model (IDDM), introduced across two landmark papers in 2012 and 2013, which provides a probabilistic framework for inferring human intentions from observed movements — a foundational capability for robots operating alongside people. The 2013 paper alone has garnered 166 citations, reflecting its significant influence on the field. Vogt has also made notable advances in physical human-robot interaction, developing techniques that allow robots to detect and compensate for external perturbations without dedicated force sensors, instead leveraging Dynamic Mode Decomposition and machine learning — work cited over 150 times across multiple publications. His 2017 system for learning continuous interactions from human-human demonstrations extended imitation learning beyond single-actor scenarios, enabling robots to acquire socially responsive behaviors more naturally. From early work on kinesthetic bootstrapping for humanoid motor skill teaching to one-shot learning of handover behaviors, Vogt's research consistently bridges perception, learning, and physical collaboration. His cumulative body of work, spanning over 500 citations, has helped establish data-driven, interaction-aware robotics as a practical and principled discipline.

Research Focus

Key Achievements

Top Papers

1. 1
   Probabilistic movement modeling for intention inference in human–robot interaction

   166 citations · 2013
2. 2
   Estimation of perturbations in robotic behavior using dynamic mode decomposition

   124 citations · 2015
3. 3
   A system for learning continuous human-robot interactions from human-human demonstrations

   69 citations · 2017
4. 4
   Probabilistic Modeling of Human Movements for Intention Inference

   42 citations · 2012
5. 5
   Kinesthetic Bootstrapping: Teaching Motor Skills to Humanoid Robots through Physical Interaction

   35 citations · 2009
6. 6
   Learning responsive robot behavior by imitation

   32 citations · 2013
7. 7
   Dynamic Mode Decomposition for perturbation estimation in human robot interaction

   27 citations · 2014
8. 8
   One-shot learning of human–robot handovers with triadic interaction meshes

   26 citations · 2018
9. 9
   Inferring guidance information in cooperative human-robot tasks

   26 citations · 2013
10. 10
    Experience-based torque estimation for an industrial robot

    13 citations · 2016

Key Collaborators

Contact & Links