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Research area Cybersecurity On-device machine learning (ML) is rapidly gaining popularity on mobile devices. Mobile developers can use on-device ML to enable ML features at users’ mobile devices
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Research area Cybersecurity On-device machine learning (ML) is rapidly gaining popularity on mobile devices. Mobile developers can use on-device ML to enable ML features at users’ mobile devices
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behaviour. This behaviour and the drawings produced might not be the same as what humans would do. At SensiLab we have developed mobile robots that can draw. These robots serve as an experimental platform
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to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a centralised server. Because data never leaves from user clients
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systematic benchmarking, followed by deployment on humanoid or mobile robots using NVIDIA Jetson edge computing platforms. This provides an opportunity to study not only accuracy, but also reliability
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interaction using humanoid and mobile robotic platforms. Aim/outline The aim of this project is to develop multimodal world models that allow embodied agents to understand human behaviour, anticipate how