All Outputs (5)

LABERT: A Combination of Local Aggregation and Self-Supervised Speech Representation Learning for Detecting Informative Hidden Units in Low-Resource ASR Systems (2023)
Conference Proceeding

With advances in deep learning methodologies, Automatic Speech Recognition (ASR) systems have seen impressive results. However, ASR in Low-Resource Environments (LREs) are challenged by a lack of training data for the specific target domain. We propo... Read More about LABERT: A Combination of Local Aggregation and Self-Supervised Speech Representation Learning for Detecting Informative Hidden Units in Low-Resource ASR Systems.

TAS for Cats: An Artist-led Exploration of Trustworthy Autonomous Systems for Companion Animals (2023)
Conference Proceeding

Cat Royale is an artist-led exploration of trustworthy autonomous systems (TAS) created by the TAS Hub's creative ambassadors Blast Theory. A small community of cats inhabits a purpose built 'cat utopia' at the centre of which a robot arm tries to en... Read More about TAS for Cats: An Artist-led Exploration of Trustworthy Autonomous Systems for Companion Animals.

Somabotics Toolkit for Rapid Prototyping Human-Robot Interaction Experiences using Wearable Haptics (2023)
Presentation / Conference

This work-in-progress paper presents a prototyping toolkit developed to design haptic interaction experiences. With developments in wearable and sensor technologies, new opportunities arise everyday to create rich haptic interaction experiences actin... Read More about Somabotics Toolkit for Rapid Prototyping Human-Robot Interaction Experiences using Wearable Haptics.

In-the-Wild Failures in a Long-Term HRI Deployment (2023)
Presentation / Conference

Failures are typical in robotics deployments "in-the-wild", especially when robots perform their functions within social human spaces. This paper reports on the failures of an autonomous social robot called Lindsey, which has been used in a public mu... Read More about In-the-Wild Failures in a Long-Term HRI Deployment.

Resolving conflicts during human-robot co-manipulation (2023)
Conference Proceeding

This paper proposes a machine learning (ML) approach to detect and resolve motion conflicts that occur between a human and a proactive robot during the execution of a physically collaborative task. We train a random forest classifier to distinguish b... Read More about Resolving conflicts during human-robot co-manipulation.