Christian Mandery

About

Christian Mandery is a robotics and human-motion researcher whose work bridges the gap between human movement science, humanoid robotics, and natural language processing. He is perhaps best known for developing the KIT Whole-Body Human Motion Database (2015), a landmark resource that has garnered over 200 citations and become a foundational tool for researchers working in motion analysis, imitation learning, and rehabilitation robotics. Alongside this, his Master Motor Map (MMM) framework established a unified standard for capturing, representing, and reproducing human motion on humanoid platforms, addressing a long-standing fragmentation problem in the field. Mandery's research consistently pursues the unification of motion representations at scale, as reflected in his 2016 work on large-scale whole-body motion databases, which has accumulated over 115 citations. His 2018 contribution on bidirectional mapping between whole-body motion and natural language using deep recurrent neural networks — with 122 citations — demonstrates his innovative crossover into machine learning and human-robot communication. Additional contributions span multi-contact humanoid locomotion planning, affordance-based motion synthesis, and even human-robot social interaction. Collectively, his work has profoundly shaped how researchers model, generate, and understand human and humanoid whole-body motion.

Research Focus

Key Achievements

Top Papers

1. 1
   The KIT whole-body human motion database

   209 citations · 2015
2. 2
   Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks

   122 citations · 2018
3. 3
   Unifying Representations and Large-Scale Whole-Body Motion Databases for Studying Human Motion

   115 citations · 2016
4. 4
   Master Motor Map (MMM) — Framework and toolkit for capturing, representing, and reproducing human motion on humanoid robots

   77 citations · 2014
5. 5
   Is hugging a robot weird? Investigating the influence of robot appearance on users' perception of hugging

   26 citations · 2016
6. 6
   A whole-body support pose taxonomy for multi-contact humanoid robot motions

   15 citations · 2017
7. 7
   Using language models to generate whole-body multi-contact motions

   8 citations · 2016
8. 8
   The Karlsruhe ARMAR Humanoid Robot Family

   7 citations · 2018
9. 9
   On the Dualities Between Grasping and Whole-Body Loco-Manipulation Tasks

   6 citations · 2017
10. 10
    Affordance-Based Multi-Contact Whole-Body Pose Sequence Planning for Humanoid Robots in Unknown Environments

    6 citations · 2018

Key Collaborators

Contact & Links