Papers
11
Total Citations
226
H-Index
7
About
Alexandre Coninx is a researcher working at the intersection of robotics, machine learning, and human-robot interaction, with a particular focus on open-ended and developmental learning in autonomous systems. His most influential work explores how robots can sustain meaningful, long-term interactions with children, notably through multi-activity switching strategies that keep young users engaged in learning and therapeutic contexts — a paper that has garnered over 100 citations and established him as a notable voice in social robotics. Coninx has also made significant contributions to the theoretical foundations of open-ended learning, proposing conceptual frameworks grounded in representational redescription that bridge reinforcement learning and developmental robotics, earning over 60 citations. His broader research agenda addresses how robots can autonomously build affordance maps, learn from demonstration, and acquire state representations in unstructured environments. More recently, he has explored few-shot quality-diversity optimization, extending meta-learning principles into evolutionary and behavioral repertoire frameworks. Across his work, Coninx consistently pursues a core question: how can robots become genuinely adaptive agents capable of learning and operating meaningfully beyond tightly controlled laboratory conditions.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Semantic-based interaction for teaching robot behavior compositions11 citations · 2017
- 4Few-Shot Quality-Diversity Optimization11 citations · 2022
- 5Behavioral accommodation towards a dance robot tutor10 citations · 2014
- 6
- 7Building an Affordances Map With Interactive Perception8 citations · 2022
- 8State Representation Learning from Demonstration5 citations · 2020
- 9ADAPTIVE MOTIVATION IN A BIOMIMETIC ACTION SELECTION MECHANISM4 citations · 2013
- 10