Distributed Robot Learning

Imagine robots gracefully navigating complex environments, meticulously sorting and packaging items, and adeptly performing intricate tasks with remarkable precision. These applications underscore the transformative potential of this research, enhancing efficiency and accuracy in multiple domains.

Current advancements in robotics leverage extensive datasets of robot trajectories, images, and language descriptions to train machine learning models, such as transformers, enabling robots to execute common tasks like picking and placing. However, these models are predominantly trained on everyday tasks involving common objects, resulting in vast datasets comprising hundreds of thousands of training episodes.

A significant challenge in robotics is the scarcity of large, specialised datasets. In many specialised fields, such as healthcare, it is impractical to collect extensive datasets due to the uniqueness and complexity of the tasks. Therefore, the critical challenge is to develop models that can learn robot tasks from only a few demonstrations. Solving this problem is vital as it would allow for the deployment of robotic systems in areas where data is limited, thereby enhancing the capabilities and utility of robotic assistance in healthcare and beyond.