Marcelo Kallmann

About

Marcelo Kallmann is a leading researcher in humanoid robotics and computer animation, whose work bridges the gap between autonomous motion planning and realistic virtual human behavior. His major contributions center on developing sampling-based motion planners that enable humanoid robots and virtual characters to perform complex, whole-body tasks in dynamic environments. He pioneered techniques for learning humanoid reaching tasks (28 citations) and planning the sequencing of movement primitives (18 citations), drawing on neuroscience principles to reduce the dimensionality of motor control. Kallmann also made significant advances in motion capture, designing an untethered system using inertial sensors for real-time humanoid teleoperation (22 citations). His work on human-aware coverage planning (10 citations) and skill-based motion planning frameworks (7 citations) further demonstrates his ability to integrate probabilistic models with parametric motion skills. Through his research, Kallmann has addressed the fundamental challenge of achieving humanlike coordination and autonomy in both physical robots and animated characters, making his work essential reading for anyone interested in the intersection of robotics, artificial intelligence, and computer graphics.

Research Focus

Key Achievements

Top Papers

1. 1
   Learning humanoid reaching tasks in dynamic environments

   28 citations · 2007
2. 2
   Motion planning and autonomy for virtual humans

   27 citations · 2008
3. 3
   Motion capture from inertial sensing for untethered humanoid teleoperation

   22 citations · 2005
4. 4
   Planning the Sequencing of Movement Primitives

   18 citations · 2004
5. 5
   Planning humanlike actions in blending spaces

   10 citations · 2011
6. 6
   THE CHALLENGE OF MOTION PLANNING FOR SOCCER PLAYING HUMANOID ROBOTS

   7 citations · 2008
7. 7
   A skill-based motion planning framework for humanoids

   7 citations · 2010

Key Collaborators

Contact & Links