Mathias Sunardi

About

Mathias Sunardi is a pioneering researcher at the intersection of robotics, performing arts, and human-robot interaction, with a core focus on expressive motion synthesis for social and entertainment robots. His most significant contribution is the development of algebraic frameworks—including Event Expressions (EE) and Event Diagrams (ED)—that allow animators and engineers to describe complex, natural robot behaviors using concise, formal notation rather than labor-intensive manual keyframing. This work directly addresses the challenge of creating unique, interesting motions for robot actors in theatre and social settings, a problem he has explored since his foundational 2000 thesis on Robot Theatre. Sunardi’s research spans from early work on emotional mimicking in humanoid bipeds, controlled via constraint-satisfaction models, to more recent advances in using music information to drive expressive robot motion. With his most-cited paper, "Music to Motion" (2017), garnering 10 citations, and a consistent stream of work on behavior expression and synthesis, Sunardi has established a clear and lasting impact on how roboticists think about movement as a language—making him a key figure in the quest to give robots the same expressive proficiency as human performers.

Research Focus

Key Achievements

Top Papers

1. 1
   Music to Motion: Using Music Information to Create Expressive Robot Motion

   10 citations · 2017
2. 2
   On Synthesis and Verification from Event Diagrams in a Robot Theatre Application

   5 citations · 2013
3. 3
   Behavior Expressions for Social and Entertainment Robots

   5 citations · 2020
4. 4
   An Emotional Mimicking Humanoid Biped Robot and its Quantum Control Based on the Constraint Satisfaction Model

   5 citations · 2007
5. 5
   Expressive Motion Synthesis for Robot Actors in Robot Theatre

   5 citations · 2000
6. 6
   Synthesizing Expressive Behaviors for Humanoid Robots

   2 citations · 2000

Key Collaborators

Contact & Links