Daniel Bak is a postgraduate researcher in the Department of Engineering, King’s College London. He is pursuing a PhD in robotics engineering under the supervision of Dr Shan Luo, in collaboration with Unilever Ltd through an EPSRC IDLA studentship. His research focuses on developing a tactile robot capable of assessing hair through human-like touch. His goal is to investigate how tactile sensing, active perception and adaptive robotic manipulation can be combined to characterise properties of hair through physical interaction.

Prior to starting his PhD, Daniel obtained an MSc in Mechanical Engineering from University College London (UCL) in 2023 and a BEng in Mechanical Engineering from Coventry University in 2021, graduating with First-Class Honours. His bachelor’s degree also included a one-year industrial placement.

Daniel's master’s dissertation, supervised by Dr Sara Adela Abad Guaman, involved developing a FEA benchmarking framework for passive compliant joints in bioinspired robotic hoof feet. This project strengthened his interest in robotics and inspired him to pursue doctoral research in the field. Before commencing his PhD, Daniel also gained professional experience in several engineering roles across the UK. 

Research Interests

• Visuo-tactile robotics
• Robot visuo-tactile sensing
• Multimodal robot perception
• Robot learning for grasping and manipulation
• Active tactile perception of deformable materials
• Adaptive robotic manipulation
• Compliant mechanisms and robotic end-effectors

Thesis Title

Development of A Tactile Robot that can Feel Hair like a Human.

Abstract: Humans rely on tactile sensing to assess the properties of materials in their surroundings. For example, when running our fingers over our hair, we instinctively adjust our movements—varying pressure, speed, and contact angle—to perceive texture, smoothness, or elasticity. This interactive process involves both sensory perception and behavioural control, as different exploration strategies can yield different sensory outcomes.

This project aims to develop a robotic system equipped with advanced tactile sensing and AI-driven decision-making to assess material properties through physical interaction. Daniel’s research will explore the interplay between tactile data acquisition and robot behaviour, investigating how a robot can actively adjust its movements to improve material perception. Key challenges include integrating high-resolution tactile sensors with AI algorithms for perception, designing adaptive exploration strategies, and ensuring robust performance across diverse materials and interaction conditions.

Supervisor Team

First Supervisor: Dr Shan Luo

Second Supervisor: Dr Emmanouil Spyrakos Papastavridis