Presentations

• “Dynamic Trajectory Planning for Robotic Manipulation in Unstructured Environments” (poster) – AgriFoRwArdS CDT Annual Conference 2024: Robots in Action [July 2024] – Norwich, UK.
• “Exploration of LLM-Enhanced State-Machine function-calls for Planning Robot Actions” (oral) – AgriFoRwArdS CDT Summer School: Robotic Phenotyping [July 2024] – Wageningen, The Netherlands.
• “Adaptive Hybrid Control Architectures for LLM-Controlled Robot Arm” (poster) – AgriFoRwArdS CDT Summer School: Robotic Phenotyping [July 2024] – Wageningen, The Netherlands.

Activities and Outputs

• Member of the AgriFoRwArdS CDT Summer School 2024 Organisation Committee (Feb to Jul 2024)

MSc Project

Adaptive Hybrid Control Architectures for LLM-Controlled Robot Arm

This project seeks to develop an adaptive control architecture that leverages the capabilities of Large Language Models (LLMs) for enhanced decision-making. By dynamically selecting and switching between various learning or planning methods—such as reinforcement learning and imitation learning—based on real-time environmental data, the architecture will enable robots to perform complex tasks more efficiently in unpredictable settings. The initiative promises to improve robotic performance, thereby enabling robots to understand and execute task instructions with unprecedented flexibility and accuracy.

PhD Project

Title to be confirmed