Leszek Pecyna
University of Liverpool, King's College London, University of Plymouth
Papers
5
Total Citations
47
H-Index
4
About
Leszek Pecyna is a robotics researcher whose work spans multimodal sensory integration, robotic manipulation, and neuro-robotics modeling, with particular emphasis on enabling robots to interact more naturally and effectively with complex environments. He is perhaps best known for his highly cited 2022 work on visual-tactile multimodality for tracking deformable linear objects using reinforcement learning (24 citations), which demonstrated how combining vision and touch can overcome the individual limitations of each sensing modality — a significant advance in manipulation of challenging, real-world objects. His subsequent research on multi-modal sensing for robotic insertion tasks in laboratory settings (7 citations) highlights his applied focus on automating repetitive experimental workflows in chemistry research, bridging robotics and scientific discovery. Earlier in his career, Pecyna made notable contributions to neuro-robotics and developmental cognition, exploring how robots can learn to count and gesture in ways that mirror child development. His 2019 deep neural network model for finger counting and numerosity estimation (7 citations) and related works on pointing and counting demonstrate a sustained interest in embodied cognition and how physical interaction shapes numerical learning. Across his body of work, Pecyna consistently pushes boundaries at the intersection of perception, learning, and robotic embodiment.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3A Deep Neural Network for Finger Counting and Numerosity Estimation7 citations · 2019
- 4
- 5Influence of Pointing on Learning to Count: A Neuro-Robotics Model4 citations · 2018