Towards perceptive and self-aware robots

机器人通常应用于制造和组装过程中，执行焊接、喷漆、组装、拾取和放置以及包装等任务，仅举几例。这些任务通常在与人类工作空间分开的受限空间中执行。然而，研究人员正在推动让机器人更具协作性，并让它们更接近人类，在不同的活动中为它们提供支持。从工业到人类工作空间的这种转变并不容易：人类环境可能是杂乱无章的，预计机器人将执行几项任务，而不会损害人类或环境本身。为了能够在这种条件下安全地执行操作，机器人必须配备适当的传感功能，使它们能够检测物体或人类的存在，或者跟踪它们的运动，以预测可能的影响。此外，机器人应该能够通过与其互动来探索周围的空间，并操纵和识别物体。在这种情况下，适当地控制接触过程中产生的力就变得至关重要。在现有技术中，这两个方面通常是分开处理的。事实上，大多数工作都建议使用摄像头或激光雷达传感器来控制机器人以避免碰撞[1-3]。然而，影响并不总是可以避免的，可能需要机器人适当地处理这些影响，以安全地与环境和人类互动。最近的研究表明，与环境的接触可以用触觉传感器测量，并被利用来在非结构化空间执行任务[4-6]或识别或处理物体[7-11]。虽然这些研究提出了应用受控相互作用的方法，但它们并没有考虑以受控的方式接近对象以避免高冲击力的问题。在这方面，融合不同类型的感知模式提供的信息来弥补一个反馈和另一个反馈的缺失是很有意义的。例如，可以同时利用接近和触觉反馈来提前预测可能的碰撞，并控制机器人测量并最小化由此产生的冲击力。类似地，在探索具有触觉的对象时可以使用接近信息，以适当的方式接近感兴趣的表面。这项研究项目将集中于开发利用多模式反馈来适当控制机器人在非结构化环境中执行任务的方法。将利用多模式传感来感知周围环境并检测机器人身体上发生的接触。特别是，飞行时间(ToF)和触觉传感器的使用将被考虑。这两种传感模式将结合在一起，为机器人提供周围区域的感知。这个想法是为了建立一个环境的增强模型，其中ToF和触觉传感器可以用来检索物体的形状和它们的机械特性。通过控制机器人执行旨在探索新区域的运动，或者区分固定或可移动的物体并测量它们的相对速度，该模型可以随着时间的推移而改进和更新。然后，可以利用机器学习技术来处理和解释更改模型状态的事件。例如，机器人可以区分可能的危险碰撞和可能的与人类的互动，并反过来触发适当的反应。更广泛地说，这将允许实现解释多模式反馈的方法，并可以控制机器人执行不同的动作。该项目与“EPSRC人工智能和机器人学”主题很好地结合在一起，因为它旨在开发新的方法，使机器人能够感知环境，以其进化的方式和相应的行为。

Robots are typically applied in manufacturing and assembly processes to perform tasks such as welding, painting, assembly, pick and place and packing just to name a few. These tasks are usually executed in a confined space separated from the human workspace. However, researchers are pushing to make robots more collaborative and to bring them closer to humans, supporting them in different activities. This transition from industry to the human workspace is not easy: the human environment could be unstructured and cluttered, and robots are expected to perform several tasks without harming the humans or the environment itself.To be able to safely execute operations in such conditions, robots must be equipped with proper sensing capabilities that allow them to detect the presence of objects or humans or track their motions to anticipate possible impacts. Furthermore, the robot should be able to explore the surrounding space by interacting with it and manipulate and recognise objects. In this case, it becomes paramount to properly control the forces arising during the contact.These two aspects are usually treated separately in the state of the art. Indeed, most of the work proposes to control robots to avoid collisions using cameras or lidar sensors [1-3]. However, impacts cannot always be avoided, and robots could be required to properly deal with them to safely interact with the environment and humans. Recent work has shown that contact with the environment can be measured with tactile sensors and exploited to perform tasks in unstructured space [4-6] or to identify or handle objects [7-11]. Although these studies propose methods to apply controlled interactions, they do not consider the problem of approaching objects in a controlled manner to avoid high-impact forces.In this respect, it would be of interest to fuse information provided by diverse types of sensing modalities to compensate for the lack of one feedback with another one. As an example, proximity and tactile feedback could be exploited together to predict in advance a possible collision and to control the robot to measure and minimise the resulting impact force. Similarly, proximity information can be used while exploring the object with the sense of touch to approach the surface of interest in a proper way. This research project will focus on the development of methods that take advantage of multimodal feedback to properly control the robot in the execution of tasks in unstructured environments. Multimodal sensing will be exploited to perceive the surroundings and to detect contacts occurring on the robot body. In particular, the use of Time of Flight (ToF) and tactile sensors will be considered. The two sensing modalities will be combined to provide the robot with awareness of its surrounding area. The idea is to build an augmented model of the environment where ToF and tactile sensors can be used to retrieve the shape of the objects and their mechanical properties. This model can be refined and updated over time by controlling the robot to perform movements aimed at exploring new areas or to distinguish between fixed or movable objects and measure their relative speed. Machine learning techniques can be then leveraged to process and interpret the events that change the state of the model. For example, the robot can discriminate between a possible dangerous collision and a possible interaction with humans and trigger a proper reaction in turn. More in general, this would allow for implementing methods where multimodal feedback is interpreted and the robot can be controlled to perform different actions.This project is well aligned with the "EPSRC artificial intelligence and robotics" theme as it aims to develop new methodologies that will enable robots to perceive the environment, the way it evolves and behave accordingly.