Katharina Muelling

About

Katharina Muelling is a robotics and machine learning researcher whose work spans human-robot interaction, motor skill learning, and assistive robotics. She is perhaps best known for her highly influential 2018 paper "Social Attention: Modeling Attention in Human Crowds," which garnered over 700 citations and introduced methods for enabling robots to navigate complex human environments by predicting crowd trajectories with social awareness—a foundational contribution to autonomous navigation research. Muelling has made significant strides in robot learning through movement primitives, developing optimization-based frameworks and mixture models that allow robots to acquire and generalize complex motor skills. Her pioneering work on robot table tennis demonstrated how imitation learning, reinforcement learning, and dynamic movement primitives can be combined to master fast, precise, multi-phase tasks—a benchmark achievement in skill learning research. A particularly impactful dimension of her work bridges robotics and neuroscience. Her research on brain-machine interfaces blended with autonomous vision-guided control has advanced neuroprosthetic arm performance, offering meaningful progress for individuals with upper limb impairments. Her continued work on shared autonomy and assistive control reflects a commitment to making intelligent robotic systems genuinely beneficial to human users—positioning her as a leading voice at the intersection of machine learning, motor control, and human-centered robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Social Attention: Modeling Attention in Human Crowds

   703 citations · 2018
2. 2
   Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping

   113 citations · 2016
3. 3
   Learning table tennis with a Mixture of Motor Primitives

   106 citations · 2010
4. 4
   Autonomy infused teleoperation with application to brain computer interface controlled manipulation

   99 citations · 2017
5. 5
   Learning strategies in table tennis using inverse reinforcement learning

   70 citations · 2014
6. 6
   Movement primitives via optimization

   54 citations · 2015
7. 7
   Learning from Demonstration for Real-Time User Goal Prediction and Shared Assistive Control

   20 citations · 2021
8. 8
   Autonomy Infused Teleoperation with Application to BCI Manipulation

   18 citations · 2015
9. 9
   Learning to Select and Generalize Striking Movements in Robot Table Tennis

   15 citations · 2012
10. 10
    Learning throwing and catching skills

    15 citations · 2012

Key Collaborators

Contact & Links