Heriot-Watt University

**Note: Project availability subject to collaboration agreement being signed**

Codeplay https://www.codeplay.com/ is a company in Edinburgh with industry expertise with compiler construction and processor architectures. They are a leading partner in the standardisation of SYCL, a programming abstraction for heterogeneous hardware. Codeplay have SYCL implementations for CPUs and GPUs.

The Codeplay CEO, Andrew Richards, founded the company in 2002. He chairs the working group for the SYCL standard within the Khronos Group.

Subsea inspection of structures is now commercial and the next frontier in subsea robotics is the safe physical interactions with underwater structures. This requires the development of new control algorithms with force compliance which can take into account external disturbances. There is also a need to work across a variety of control models, from full teleoperation across high bandwidth data to shared autonomy (our goal) across low and intermittent connection to enable shore-based control of remote platforms.

The Internet-of-Robotic-Things (IoRT) brings together autonomous robotic systems with the Internet of Things (IoT) vision of sensors and smart objects pervasively embedded in everyday environments [1-4]. This merge can enable novel applications in almost every sector where cooperation between robots and IoT technology can be imagined. Early signs of this convergence are in network robot systems [5], robot ecologies [6], or in approaches such as cloud robotics [7].

Natural Language Generation (NLG) is the task of translating machine-readable representations and data into human language, and thus vital for accountability in safe human-machine collaboration. Neural Network architectures for NLG are promising since they able to capture linguistic knowledge through latent representations using raw input data, and hence have the benefit of simplifying the design of systems by avoiding costly manual engineering of features, with the potential of more easily scaling to new data and domains.

The training phase in Deep Learning is very compute and data intensive and, therefore, the efficiency of the training phase typically restricts the quality of results that can be achieved within a given time frame.

Many learning algorithms are dominated by the speed in which data can be brought to the CPU, i/e., by the memory bandwidth of the executing hardware. Consequently, techniques that are based on reduced precision number representations have been shown to produce faster results without a significant loss in the quality of results.

Key to effective facilities management and visions like Smart Cities or Digital Britain is the availability of reliable Digital Twins of built environment assets (buildings, infrastructure industrial complexes). Such semantically-rich 3D models are important to support Operations and Maintenance activities, particularly when robots are to be deployed (e.g. to operate a valve or simply navigate some environments during dangerous or emergency operations).

There are many outstanding research problems in developing spoken conversational Natural Language interfaces for effective human-robot interaction (HRI).  For example:

Wearable robots (WR) cover a wide spectrum of robotic devices that can be worn or be partially worn by human or even animals. Perhaps the most well referred WRs are assistive devices such as powered prostheses and exoskeletons.  However, with advances in miniaturisation, it is anticipated that WRs will evolve through intelligent mechanisms, software and physical hardware not just for assisting with human motion.