AMP in the wild: Learning robust, agile, natural legged locomotion skills

The successful transfer of a learned controller from simulation to the real world for a legged robot requires not only the ability to identify the system, but also accurate estimation of the robot's state. In this paper, we propose a novel algorithm that can infer not only information about the parameters of the dynamic system, but also estimate important information about the robot's state from previous observations. We integrate our algorithm with Adversarial Motion Priors and achieve a robust, agile, and natural gait in both simulation and on a Unitree A1 quadruped robot in the real world. Empirical results demonstrate that our proposed algorithm enables traversing challenging terrains with lower power consumption compared to the baselines. Both qualitative and quantitative results are presented in this paper.Comment: Video: https://youtu.be/7Ggcj6Izfh

Interactions between 0-Dimensional and 2-Dimensional Materials

This thesis describes two types of interactions between zero-dimensional and two-dimensional materials: energy transfer and surface diffusion. The first chapter introduces zero-dimensional and two-dimensional materials and their unique properties. Based on emerging properties different from bulk materials', several attempts have been shown to study the interaction between these two classes of materials. The second chapter presents the study on the energy transfer between zero-dimensional and two-dimensional materials, specifically semiconductor nanocrystals (or "quantum dots") and graphene. The fluorescence quenching was observed for quantum dots on graphene compared those in the absence of graphene. The strong energy transfer is through Coulomb interaction in the way similar to Forster resonant energy transfer. Based on simple assumptions, energy transfer between quantum dots and single-layer graphene was extended to quantum dots and few-layer graphene and quantitative agreement was achieved between experimental results and calculation from theory. The third chapter investigates the surface diffusion of zero-dimensional materials on a two-dimensional material. Metal adatoms diffuse on graphene and form different nanostructures depending on the supporting substrate for graphene. As a atomically thin material, graphene is susceptible to change in underlying supporting substrates. This susceptibility will introduce surface corrugation, chemical reactivity and electron-hole puddles to graphene, and finally will lead to different morphology of metal nanoparticles on graphene. Using classical nucleation theory, different diffusion constants of Au adatoms were reported on graphene supported by different substrate. Two major factors are identified to explain the difference: surface corrugation and π electronic stabilization. In the final chapter, the characterization of zero-dimensional and two-dimensional materials is discussed. It is mainly done using Raman spectroscopy, which is a non-destructive tool. Without knowing the pristine properties of materials, their interactions with other materials are beyond reach

Data mining for fault diagnosis in steel making process under industry 4.0

The concept of Industry 4.0 (I4.0) refers to the intelligent networking of machines and processes in the industry, which is enabled by cyber-physical systems (CPS) - a technology that utilises embedded networked systems to achieve intelligent control. CPS enable full traceability of production processes as well as comprehensive data assignments in real-time. Through real-time communication and coordination between "manufacturing things", production systems, in the form of Cyber-Physical Production Systems (CPPS), can make intelligent decisions. Meanwhile, with the advent of I4.0, it is possible to collect heterogeneous manufacturing data across various facets for fault diagnosis by using the industrial internet of things (IIoT) techniques. Under this data-rich environment, the ability to diagnose and predict production failures provides manufacturing companies with a strategic advantage by reducing the number of unplanned production outages. This advantage is particularly desired for steel-making industries. As a consecutive and compact manufacturing process, process downtime is a major concern for steel-making companies since most of the operations should be conducted within a certain temperature range. In addition, steel-making consists of complex processes that involve physical, chemical, and mechanical elements, emphasising the necessity for data-driven approaches to handle high-dimensionality problems. For a modern steel-making plant, various measurement devices are deployed throughout this manufacturing process with the advancement of I4.0 technologies, which facilitate data acquisition and storage. However, even though data-driven approaches are showing merits and being widely applied in the manufacturing context, how to build a deep learning model for fault prediction in the steel-making process considering multiple contributing facets and its temporal characteristic has not been investigated. Additionally, apart from the multitudinous data, it is also worthwhile to study how to represent and utilise the vast and scattered distributed domain knowledge along the steel-making process for fault modelling. Moreover, state-of-the-art does not iv Abstract address how such accumulated domain knowledge and its semantics can be harnessed to facilitate the fusion of multi-sourced data in steel manufacturing. In this case, the purpose of this thesis is to pave the way for fault diagnosis in steel-making processes using data mining under I4.0. This research is structured according to four themes. Firstly, different from the conventional data-driven research that only focuses on modelling based on numerical production data, a framework for data mining for fault diagnosis in steel-making based on multi-sourced data and knowledge is proposed. There are five layers designed in this framework, which are multi-sourced data and knowledge acquisition, data and knowledge processing, KG construction and graphical data transformation, KG-aided modelling for fault diagnosis and decision support for steel manufacturing. Secondly, another of the purposes of this thesis is to propose a predictive, data-driven approach to model severe faults in the steel-making process, where the faults are usually with multi-faceted causes. Specifically, strip breakage in cold rolling is selected as the modelling target since it is a typical production failure with serious consequences and multitudinous factors contributing to it. In actual steel-making practice, if such a failure can be modelled on a micro-level with an adequately predicted window, a planned stop action can be taken in advance instead of a passive fast stop which will often result in severe damage to equipment. In this case, a multifaceted modelling approach with a sliding window strategy is proposed. First, historical multivariate time-series data of a cold rolling process were extracted in a run-to-failure manner, and a sliding window strategy was adopted for data annotation. Second, breakage-centric features were identified from physics-based approaches, empirical knowledge and data-driven features. Finally, these features were used as inputs for strip breakage modelling using a Recurrent Neural Network (RNN). Experimental results have demonstrated the merits of the proposed approach. Thirdly, among the heterogeneous data surrounding multi-faceted concepts in steelmaking, a significant amount of data consists of rich semantic information, such as technical documents and production logs generated through the process. Also, there Abstract v exists vast domain knowledge regarding the production failures in steel-making, which has a long history. In this context, proper semantic technologies are desired for the utilisation of semantic data and domain knowledge in steel-making. In recent studies, a Knowledge Graph (KG) displays a powerful expressive ability and a high degree of modelling flexibility, making it a promising semantic network. However, building a reliable KG is usually time-consuming and labour-intensive, and it is common that KG needs to be refined or completed before using in industrial scenarios. In this case, a fault-centric KG construction approach is proposed based on a hierarchy structure refinement and relation completion. Firstly, ontology design based on hierarchy structure refinement is conducted to improve reliability. Then, the missing relations between each couple of entities were inferred based on existing knowledge in KG, with the aim of increasing the number of edges that complete and refine KG. Lastly, KG is constructed by importing data into the ontology. An illustrative case study on strip breakage is conducted for validation. Finally, multi-faceted modelling is often conducted based on multi-sourced data covering indispensable aspects, and information fusion is typically applied to cope with the high dimensionality and data heterogeneity. Besides the ability for knowledge management and sharing, KG can aggregate the relationships of features from multiple aspects by semantic associations, which can be exploited to facilitate the information fusion for multi-faceted modelling with the consideration of intra-facets relationships. In this case, process data is transformed into a stack of temporal graphs under the faultcentric KG backbone. Then, a Graph Convolutional Networks (GCN) model is applied to extract temporal and attribute correlation features from the graphs, with a Temporal Convolution Network (TCN) to conduct conceptual modelling using these features. Experimental results derived using the proposed approach, and GCN-TCN reveal the impacts of the proposed KG-aided fusion approach. This thesis aims to research data mining in steel-making processes based on multisourced data and scattered distributed domain knowledge, which provides a feasibility study for achieving Industry 4.0 in steel-making, specifically in support of improving quality and reducing costs due to production failures

Multi-sourced modelling for strip breakage using knowledge graph embeddings

Strip breakage is an undesired production failure in cold rolling. Typically, conventional studies focused on cause analyses, and existing data-driven approaches only rely on a single data source, resulting in a limited amount of information. Hence, we propose an approach for modelling breakage using multiple data sources. Many breakage-relevant features from multiple sources are identified and used, and these features are integrated using a breakage-centric ontology which is then used to create knowledge graphs. Through ontology construction and knowledge embedding, a real-world study using data from a cold-rolled strip manufacturer was conducted using the proposed approach

A multi-source feature-level fusion approach for predicting strip breakage in cold rolling

As an undesired and instantaneous failure in the production of cold-rolled strip products, strip breakage results in yield loss, reduced work speed and further equipment damage. Typically, studies have investigated this failure in a retrospective way focused on root cause analyses, and these causes are proven to be multi-faceted. In order to model the onset of this failure in a predictive manner, an integrated multi-source feature-level approach is proposed in this work. Firstly, by harnessing heterogeneous data across the breakage-relevant processes, blocks of data from different sources are collected to improve the breadth of breakage-centric information and are pre-processed according to its granularity. Afterwards, feature extraction or selection is applied to each block of data separately according to the domain knowledge. Matrices of selected features are concatenated in either flattened or expanded manner for comparison. Finally, fused features are used as inputs for strip breakage prediction using recurrent neural networks (RNNs). An experimental study using real-world data instantaneouseffectiveness of the proposed approach