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

问题陈述：与工程和计算机科学领域相比，生物学和化学领域在任务的工具和自动化方面装备严重不足。自动化任务扫描的能力有助于减少人为错误并提高实验率。自动化还可以使优化框架应用于任务，这些任务通常可以重新构建我们如何看待流程。已经进行了研究以使这一过程自动化。目前的这些方法在创造人类和机器人之间的协作环境以实现实验室工作自动化方面的潜力不足。本文中提出的工作依赖于商业上可用的机器人平台，这严重限制了合作的潜力;这种限制因通常为人类使用而设计的实验室环境而加剧。因此，这些机器人设计是硬塞进环境中的。自动化过程，如中提出的，为特定过程的快速自动化提供了希望。然而，化学和生物学涉及复杂的大量任务，这些任务在不同的实验中会略有不同。目前，用于化学和生物实验室中任务自动化的现成解决方案通常配备有针对非常特定任务的操纵器。这些机械手不适合脆弱和困难的实验室工作拓扑。通常，这是因为生物学和化学中使用的许多工具都是为了与人类互动而设计的。灵巧操作是机器人研究的一个领域，可以直接应用于化学和生物实验室工作的复杂操作挑战。OpenAI [5]的最新研究已经使用强化学习进行灵巧操作，用于手操作策略。在模拟环境中，以高水平的随机性学习灵巧操作的过程。然而，最近提出的工作，证明了高水平的鲁棒性与传感器，纯粹的兼容性的方法。使用机械衍生的漏斗，最初提出的，被扩展为使用的运动原语组成，使操作任务可以通过顺序的运动原语执行。另一个领域，可以纳入解决灵巧的操作任务的实验室工作，是互动的看法。基于前面提到的运动鲁棒性，可以利用交互式感知来扩展实验室自动化机器人对不同工具的鲁棒性。在实验室中使用的工具集是一个相对有限的组，然而，工具的子集的变化是这样的，纯感知模型可能不足够强大的无故障交互。实现无故障的实验室环境是值得注意和非常有价值的，因为考虑到许多情况下，不良的化学或生物处理将导致危险的后果，部署的任何系统都必须非常强大。

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 taskscan 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.