Davide Bacciu
Papers
17
Total Citations
436
H-Index
9
About
Davide Bacciu is a leading researcher at the intersection of artificial intelligence, robotics, and pervasive computing. His work focuses on creating intelligent, adaptive systems that can learn and operate autonomously in complex, real-world environments. Bacciu’s major contributions span three key areas: continual learning for neural networks, autonomous robotic manipulation with soft hands, and the emerging paradigm of the Internet of Robotic Things (IoRT). His most cited work, a 2021 study on continual learning for recurrent neural networks (122 citations), provides a critical empirical evaluation of how AI systems can learn new tasks without forgetting previous knowledge. In robotics, his data-driven architecture for autonomous grasping with anthropomorphic soft hands (90 citations) has advanced the field by enabling robots to learn from human demonstrations, leveraging the inherent adaptability of compliant robotic systems. Bacciu also pioneered the concept of cognitive robotic ecologies for smart homes, creating self-configuring ambient intelligence systems that integrate mobile robots with environmental sensors. With over 400 total citations, his research has been instrumental in bridging the gap between machine learning theory and practical robotic applications, particularly in assistive living and autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1Continual learning for recurrent neural networks: An empirical evaluation122 citations · 2021
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- 4Robotic Ubiquitous Cognitive Ecology for Smart Homes35 citations · 2015
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- 6Learning context-aware mobile robot navigation in home environments24 citations · 2014
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- 8A General Purpose Distributed Learning Model for Robotic Ecologies13 citations · 2012
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