Papers
5
Total Citations
99
H-Index
4
About
Sophie Sakka is a robotics researcher whose work sits at the intersection of autonomous systems, humanoid robotics, and sensorimotor learning. Her research focuses on enabling robots to develop internal models of themselves and their environments through adaptive, learning-based approaches — a challenge central to building truly autonomous machines. Sakka's most influential contribution is her work on active motor babbling for sensorimotor learning, which has accumulated 73 citations and proposes a method by which humanoid robots can autonomously acquire body and environmental models through self-directed exploration — drawing a compelling parallel to how infants develop motor skills. This line of research extends across several complementary studies, including her investigations into sensory prediction learning and change detection, which together form a coherent framework for robot self-modeling. Beyond learning systems, Sakka has contributed to the structural design of robotic platforms, applying genetic algorithms to the optimal configuration of mobile manipulators, and has explored motion imitation, demonstrating how humanoid robots can replicate human movement while satisfying physical balance constraints. Her body of work reflects a sustained commitment to grounding autonomous robotics in biologically inspired principles, making her a notable voice in the fields of developmental robotics and intelligent systems.
Research Focus
Key Achievements
Top Papers
- 1Active motor babbling for sensorimotor learning73 citations · 2009
- 2Sensory prediction for autonomous robots10 citations · 2007
- 3
- 4Sensory prediction learning - how to model the self and environment6 citations · 2008
- 5