Ignasi Cos

About

Ignasi Cos is a leading researcher in computational neuroscience and cognitive robotics, whose work bridges the gap between biological motivation and artificial intelligence. His primary research areas include sensorimotor integration, affordance learning, and the computational modeling of motivated behavior. Cos’s major contributions lie in formalizing how internal homeostatic drives shape perception and action. In his highly cited 2010 paper (50 citations), he introduced a groundbreaking architecture that integrates sensorimotor interaction with motivational states, enabling agents to learn object affordances for consummatory behaviors—a key step toward grounded, autonomous learning. Earlier, in his 2004 work (41 citations), Cos pioneered the use of Self-Organizing Feature Maps (SOFM) to allow agents to learn action potentials by relating invariant object properties to behavioral outcomes. This foundational work has influenced fields from developmental robotics to adaptive control. Cos’s research is notable for its interdisciplinary rigor, offering a principled framework for understanding how biological drives can guide artificial perception and learning. His work continues to inspire researchers seeking to create more adaptive, motivationally grounded intelligent systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Learning Affordances of Consummatory Behaviors: Motivation-Driven Adaptive Perception

   50 citations · 2010
2. 2
   Using a SOFM to learn Object Affordances

   41 citations · 2004

Key Collaborators

Contact & Links