Papers
12
Total Citations
201
H-Index
6
About
Maxime Petit is a robotics and cognitive systems researcher whose work sits at the intersection of developmental robotics, human-robot interaction, and cognitive architectures. His most influential contribution, the DAC-h3 architecture (2017, 74 citations), introduced a biologically grounded cognitive framework enabling humanoid robots to proactively explore and learn about their environment through mixed-initiative collaboration with humans. This foundational work reflects his broader commitment to building robots capable of genuinely cooperative, adaptive behavior. Petit has made significant strides in multimodal learning and cooperation, most notably in his highly cited 2012 study (53 citations) examining how language coordinates real-time cooperative task learning — a key insight for designing robots that can negotiate shared plans with human partners. His research further extends into autobiographical memory systems, allowing robots to accumulate and recall sensorimotor experiences over extended interactions, and into developmental Bayesian optimization, enabling autonomous self-tuning of robotic systems. Notable applied work includes a proof-of-concept for human-robot cooperation aboard the International Space Station and developmental frameworks for programming robots through natural spoken instruction — particularly valuable for users with motor impairments. Across his portfolio, Petit has helped advance the vision of robots as genuine cognitive partners in human environments.
Research Focus
Key Achievements
Top Papers
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- 3Lifelong Augmentation of Multimodal Streaming Autobiographical Memories20 citations · 2015
- 4Learning Kinematic Structure Correspondences Using Multi-Order Similarities13 citations · 2017
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- 8Towards anchoring self-learned representations to those of other agents4 citations · 2016
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