Jochen Triesch

About

Jochen Triesch is a computational neuroscientist and roboticist whose work spans human-robot interaction, developmental robotics, and active vision systems. His early research pioneered gesture-based interfaces for robotic control, with foundational contributions in real-time hand tracking and gesture recognition that earned him several highly cited papers from the late 1990s and early 2000s, including work on the GripSee gesture-controlled robot system (56 citations) and a person-independent gesture interface (68 citations). Triesch's research evolved toward understanding the computational underpinnings of human cognitive development, notably proposing influential models of how shared attention emerges through contingency learning and motivational mechanisms (63 citations) — work with significant implications for understanding autism spectrum disorders. A recurring theme in his research is the efficient coding hypothesis applied to autonomous robotic vision: his lab developed self-calibrating systems for binocular vergence, smooth pursuit, and multi-scale active vision through intrinsically motivated learning. More recently, he incorporated deep reinforcement learning into binocular fixation systems, reflecting his sustained commitment to bridging biological vision science with autonomous robotics. Across more than two decades, Triesch's interdisciplinary contributions have shaped both developmental cognitive science and adaptive robotic perception.

Research Focus

Key Achievements

Top Papers

1. 1
   A gesture interface for human-robot-interaction

   68 citations · 2002
2. 2
   Combining embodied models and empirical research for understanding the development of shared attention

   63 citations · 2003
3. 3
   Robotic gesture recognition

   61 citations · 1998
4. 4
   GripSee: A Gesture-Controlled Robot for Object Perception and Manipulation

   56 citations · 1999
5. 5
   Robust active binocular vision through intrinsically motivated learning

   41 citations · 2013
6. 6
   Self-calibrating smooth pursuit through active efficient coding

   29 citations · 2014
7. 7
   Design of an anthropomorphic robot head for studying autonomous development and learning

   27 citations · 2004
8. 8
   Autonomous learning of active multi-scale binocular vision

   24 citations · 2013
9. 9
   A robotic model of the development of gaze following

   20 citations · 2008
10. 10
    Learning of binocular fixations using anomaly detection with deep reinforcement learning

    19 citations · 2017

Key Collaborators

Contact & Links