Robotics and Autonomous Systems (UoE Lead with HWU) (PhD) - 4 Years (Full-Time)

Problem Statement: The fields of biology and chemistry are severely under-equipped compared to the fields of engineering and computer science for the tooling and automation of tasks. The ability to automate tasks
can help reduce human error and increase the rate of experimentation. Automation can also enable optimization frameworks to be applied to tasks that can often re-frame how we look at processes. Research has been conducted to automate this process. These current approaches fall short in their potential for creating a collaborative environment between humans and robots for the automation of laboratory work. The work presented in rely on a commercially available robot platform that severely limits the potential for cooperation; this limitation is exacerbated by the lab environment which is often designed for human use. As a result, these robot designs are shoe-horned into the environment. Automation processes like that proposed in offer promise for rapid automation of a specific process. However, chemistry and biology involve a complex multitude of tasks that can vary slightly from experiment to experiment. Currently, off the shelf solutions for automation of tasks in chemistry and biology labs are often equipped with manipulators geared towards very specific tasks. These manipulators are ill-suited for the fragile and difficult topology of lab work. Typically, this is because a lot of the tooling used in biology and chemistry is designed for interaction with humans. Dexterous manipulation is an area of robotics research that can be directly applied to the intricate manipulation challenge of chemistry and biology lab work. State of the art research by OpenAI [5] has been conducted into dexterous manipulation using reinforcement learning for in hand manipulation policies. The process of dexterous manipulation is learnt with high levels of randomness in a simulation environment. However, recent work proposed by, demonstrates high levels of robustness with a sensor-less, purely compliant approach. The use of mechanically derived funnels, originally proposed in, is expanded for the use of motion primitives that are composed such that manipulation tasks can be executed through sequential motion primitives. Another area that can be incorporated into solving the dexterous manipulation task for laboratory work, is interactive perception. Grounded in the motion robustness stated previously, interactive perception can be utilized to expand the robustness of a lab automation robot to different tools. The set of tools used in a lab are of a relatively finite group, however, the variations in the sub-sets of tools are such that pure perception models may not be robust enough for failure-free interaction. Enabling a failure-free lab environment is to be noted and highly worthy, as any system deployed must be extremely robust given the numerous situations where poor chemical or bio handling would lead to dangerous consequences.