Home helper robots: Understanding our future lives with human-like AI. This fellowship aims to understand and plan for the social effects of embedding ‘cute’ home helper robots into people’s everyday lives. The project is expected to generate new knowledge and resources to understand and respond to the emerging opportunities and risks associated with home helper robots, including their ability to support household tasks, and to provide child and aged care and companionship. Expected outcomes inc ....Home helper robots: Understanding our future lives with human-like AI. This fellowship aims to understand and plan for the social effects of embedding ‘cute’ home helper robots into people’s everyday lives. The project is expected to generate new knowledge and resources to understand and respond to the emerging opportunities and risks associated with home helper robots, including their ability to support household tasks, and to provide child and aged care and companionship. Expected outcomes include an improved understanding of anthropomorphised robots in everyday life and innovation in home helper robot theory and imaginaries. This should provide benefits such as informing robot design and policy to improve social outcomes, consumer protections and human-robot relationships.Read moreRead less
Learning to Reason in Reinforcement Learning. Deep Reinforcement Learning (RL) uses deep neural networks to represent and learn optimal decision-making policies for intelligent agents in complex environments. However, most RL approaches require millions of episodes to converge to good policies, making it difficult for RL to be applied in real-world scenarios taking significant resources. This project aims to equip RL with capabilities such as counterfactual reasoning and outcome anticipation to ....Learning to Reason in Reinforcement Learning. Deep Reinforcement Learning (RL) uses deep neural networks to represent and learn optimal decision-making policies for intelligent agents in complex environments. However, most RL approaches require millions of episodes to converge to good policies, making it difficult for RL to be applied in real-world scenarios taking significant resources. This project aims to equip RL with capabilities such as counterfactual reasoning and outcome anticipation to significantly reduce the number of interactions required, improve generalisation, and provide the agent with the capability to consider the cause-effects. These improvements would narrow the gap between AI and human capabilities and broaden the adoption of RL in real-world applications.Read moreRead less