Papers
32
Total Citations
509
H-Index
13
About
Daniel Polani is a prominent researcher whose work spans artificial intelligence, robotics, and information theory, with particular expertise in autonomous systems, sensorimotor learning, and agent-centered intelligence. His most influential contribution lies in developing information-theoretic frameworks that allow robots and artificial agents to learn about their own sensory and actuator systems without any innate prior knowledge — a landmark achievement demonstrated on real robotic platforms and cited over 86 times. Polani is also widely recognized for his concept of *empowerment* as a guiding principle for robot behavior, proposing it as a principled replacement for Asimov's Three Laws of Robotics, a philosophically bold contribution that has attracted significant attention in the field. His research extends into human-robot interaction, including pioneering work within the AuRoRa project exploring robots as therapeutic tools for children with autism, and gait prediction systems for rehabilitation robotics. He has also contributed to large-scale European initiatives such as the WiMUST project, advancing cooperative autonomous underwater vehicles for geophysical exploration. Across more than two decades, Polani's interdisciplinary research has shaped how scientists think about meaningful information, sensor evolution, and the foundations of embodied artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1
- 2Empowerment As Replacement for the Three Laws of Robotics41 citations · 2017
- 3RoboCup 2003: Robot Soccer World Cup VII39 citations · 2004
- 4Sensory channel grouping and structure from uninterpreted sensor data37 citations · 2004
- 5
- 6
- 7
- 8Gait trajectory prediction using Gaussian process ensembles21 citations · 2014
- 9
- 10