Dr Lavindra De Silva
Senior Research Associate
Lavindra is a Senior Research Associate in the Cambridge University Engineering Department. He joined the Distributed Information and Automation Laboratory (DIAL) in February 2019 as project leader on the Digital Manufacturing on a Shoestring project, which is investigating how low-cost, readily available digital technologies can be implemented to support growth and productivity in small to medium-sized enterprises (SMEs).
Prior to joining DIAL, Lavindra worked in the Engineering Department of the University of Nottingham, where, in collaboration with the Agents Lab, he developed algorithms and a tool for determining whether and how a factory of manufacturing cells can ‘reconfigure’ in order to manufacture a product. This enabled factories in a manufacturing cloud to automatically ‘bid’ for products requested by customers. Before coming to UK, Lavindra worked on two projects as a research scientist at the French National Center for Scientific Research (CNRS-LAAS). The first project explored how to build compliant software systems for safety-critical applications, by extending the flagship Genom middleware framework and methodology which is used for developing low-level software modules that control robot hardware components. The second project explored how to unify hierarchical planning algorithms used in AI and the geometric reasoners used in robotics to plan the detailed motions of robot joints.
Lavindra did his PhD in Australia at the RMIT University Agents Lab, where he developed a logic-based formalism for making Belief-Desire-Intention agent systems more robust. This enabled both runtime composition of novel agent behaviours from scratch in order to adapt to unforeseen situations, and the ability to perform runtime hierarchical ‘lookahead’ over behaviors, to ensure that they do not fail part way through execution.
Lavindra's research interests lie in AI and in applying AI to manufacturing and robotics. More specifically, he is interested in: developing novel hierarchical and hybrid (AI) planning approaches, including unifying the task and motion planning algorithms that are used in AI and robotics; in developing formal semantics for autonomous (and typically BDI-style) agent systems, including semantics for concurrency; and in the synthesis of provably correct controllers for robotics and manufacturing.