Papers
20
Total Citations
893
H-Index
14
About
Martin Do is a leading researcher in human motion analysis and humanoid robotics, renowned for his work in building the foundational infrastructure for data-driven robot learning. His primary research areas include whole-body motion databases, imitation learning, and human-robot interaction. Do’s most significant contribution is the creation of the KIT Whole-Body Human Motion Database, which, with over 209 citations, has become a standard resource for studying human motion synthesis, biomechanics, and rehabilitation robotics. He also developed the Master Motor Map (MMM), a unifying framework for capturing, representing, and reproducing human motion on humanoid robots, enabling seamless transfer of motor knowledge between humans and machines. His work on dynamic movement primitives (DMPs) advanced the encoding of periodic and transient motions, crucial for adaptive robot behavior in contact tasks. With over 750 total citations across his top papers, Do’s research has directly impacted the design of humanoid robots and wearable exoskeletons. Notably, he has also explored culture-adaptive humanoid behavior, such as a greeting selection system, demonstrating his commitment to socially intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1The KIT whole-body human motion database209 citations · 2015
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- 4Imitation of human motion on a humanoid robot using non-linear optimization73 citations · 2008
- 5Integrated Grasp and motion planning68 citations · 2010
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- 9Advances in Robot Programming by Demonstration35 citations · 2010
- 10A Novel Greeting Selection System for a Culture-Adaptive Humanoid Robot26 citations · 2015