Agnese is a PhD Student at the Knowledge Media Institute (KMi), the Open University, UK. Her research focuses on combining Knowledge technologies with Machine Learning to enhance the robots' ability to assist humans on their daily tasks. Specifically, she is interested in how robots can visually learn how to build a model of their environment and maintain it over time. Recently, she was part of the organising team of the 1st International Robotic competition to be held in a shopping mall (the 2019 SciRoc Challenge). Agnese received her M.S. in Information Sciences and Technology from the Pennsylvania State University.

Keys: Robotics, Computer Vision, Semantic Mapping, Lifelong Machine Learning, Information Retrieval, Knowledge Management

Team: Gianluca Bardaro, Jason Carvalho, Enrico Daga, Enrico Motta

KMi Celebrates International Women's Day 2021

Agneses presentation at KR 2020 tops the charts

Agnese secures 2nd place at the 2020 OU poster competition

First International Competition on Robots in Smart Cities held in Central Milton Keynes

SciRoc Show at MK Futures 2050

Chiatti, A., Motta, E., Daga, E. and Bardaro, G. (2021) Fit to Measure: Reasoning about Sizes for Robust Object Recognition, Proceedings of the AAAI2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021), International Virtual Event

Chiatti, A., Motta, E. and Daga, E. (2020) Towards a Framework for Visual Intelligence in Service Robotics:Epistemic Requirements and Gap Analysis, Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR 2020)

Chiatti, A., Bardaro, G., Bastianelli, E., Tiddi, I., Mitra, P. and Motta, E. (2020) Task-Agnostic Object Recognition for Mobile Robots through Few-Shot Image Matching Task-Agnostic Object Recognition for Mobile Robots through Few-Shot Image Matching, 9

Chiatti, A. (2020) Robots with common sense: Improving sensemaking in service robotics

Hoque, M., Bradley, D., Kwan, C., Chiatti, A., Li, J. and Wu, J. (2019) Searching for Evidence of Scientific News in Scholarly Big Data Searching for Evidence of Scientific News in Scholarly Big Data, pp. 251-254, ACM Press