Robotics, Autonomous Intelligence and Learning Lab
The RAIL Lab, established in 2014, is dedicated to conducting cutting-edge research in the field of artificially intelligent systems. With a focus on both fundamental and applied research, our vision is to serve as a prominent centre of excellence and a hub for AI activities in Africa. We aim to make significant contributions to the field of AI while also applying our findings to benefit society at large.
Latest Research
The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource Languages
This paper presents the Esethu Framework, a sustainable data curation framework specifically designed to empower local communities and …
M-SAT: Multi-State-Action Tokenisation in Decision Transformers for Multi-Discrete Actions
Effective decision-making in complex environments with multi-discrete action spaces poses significant challenges for agent …
Using NEAT to Learn Operators for Flexible Boolean Composition within Reinforcement Learning
Skill composition is a growing area of interest within Reinforcement Learning (RL) research. For example, if designing a robot for …
Make Haste Slowly: A Theory of Emergent Structured Mixed Selectivity in Feature Learning ReLU Networks
In spite of finite dimension ReLU neural networks being a consistent factor behind recent deep learning successes, a theory of feature …
Composition and Zero-Shot Transfer with Lattice Structures in Reinforcement Learning
An important property of long-lived agents is the ability to reuse existing knowledge to solve new tasks. An appealing approach towards …
A Linear Network Theory of Iterated Learning
Language provides one of the primary examples of human’s ability to systematically generalize — reasoning about new …