ContinualAI Research (CLAIR) is a collaborative laboratory with the goal to foster the advance of Continual Learning (CL) technologies for AI.


1. Mission
3. Introduction to CL
4. Datasets
2. People


Our mission is to support emerging and consolidated researchers and scientists to produce and disseminate original CL research through the organization of academic events and workshops, online meetups and reading groups, and blogpost publications.

Join our quest to investigate and answer the fascinating open-challenges of CL for AI:

  • What are the key principles and mechanisms governing lifelong learning in the brain?
  • How do we model artificial CL approaches that better capture the flexibility, robustness, and scalability exhibited by biological systems?
  • How do we leverage current machine learning models to synergically work on embodied agents and robots that interact with the environment?
  • How can we develop, advance, and deploy CL systems responsibly and ethically?

Introduction to CL

Here is a short list of papers to start with that summarize and discuss the state of the art. For a longer list of CL approaches, empirical studies, and applications, please check our wiki.

Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., & Díaz-Rodríguez, N. (2020). Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges. Information Fusion Journal, 58:52-68.

Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S. (2019) Continual Lifelong Learning with Neural Networks: A Review. Neural Networks 113:54-71 [arXiv:1802.07569]

Kemker, R., McClure, M., Abitino, A., Hayes, T.L., Kanan, C. (2018). Measuring Catastrophic Forgetting in Neural Networks. Thirty-Second AAAI Conference on Artificial Intelligence, 2018.

Díaz-Rodríguez, N., Lomonaco, V., Filliat, D., Maltoni, D. (2018) Don’t forget, there is more than forgetting: new metrics for Continual Learning. Continual Learning Workshop at NeurIPS, Montreal, Canada.


Datasets exclusively designed for incremental/continual (robot) learning:

CORe50: A new Dataset and Benchmark for Continual Learning and Object Recognition, Detection, Segmentation.

OpenLORIS: A Dataset and Benchmark towards Lifelong Object Recognition.


CLAIR is maintained by the following ContinualAI people:

Vincenzo Lomonaco
Co-Director and Chief Scientist
Neuroscience-inspired AI, Continual Learing, Robotics

German I. Parisi
Co-Director and Chief Scientist
Neuroscience-inspired AI, Continual Learing, Robotics

Keiland Cooper
Research Scientist
Neuroscience, Neuroscience-inspired AI, Continual Learing

Qi She
Research Scientist
Continual Learing, Robotics

Jeremy Forest
Research Scientist
Neuroscience, Neuroscience-inspired AI, Continual Learing