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In the future, robots will be a part of our daily lives, supporting us with physical tasks in the household and in factories, delivering our goods, driving our cars, and harvesting our crops. Society will receive tremendous benefits from the responsible use of robot capabilities as tools that enable and support people. Robots will closely interact with people while reasoning about human intentions. They will need to be robust to uncertainty from perception and prediction and able to reason about the environment based on a higher-level intuitive understanding of the world.
Towards this future, I am advancing the science and engineering of autonomy, especially in the presence of human interactions.
In doing so, I combine tools from controls, optimization, game theory, and machine learning. While deeply rooted in robotics, my interdisciplinary research connects control theoretic and machine learning approaches with other fields such as social and behavioral psychology and the cognitive sciences in order to achieve increasingly complex interactive behavior of robots working with and around us.
Towards this future, I am advancing the science and engineering of autonomy, especially in the presence of human interactions.
In doing so, I combine tools from controls, optimization, game theory, and machine learning. While deeply rooted in robotics, my interdisciplinary research connects control theoretic and machine learning approaches with other fields such as social and behavioral psychology and the cognitive sciences in order to achieve increasingly complex interactive behavior of robots working with and around us.
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论文共 42 篇作者统计合作学者相似作者
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CoRR (2024): 3149-3158
Peter Werner,Tim Seyde, Paul Drews, Thomas Matrai Balch,Igor Gilitschenski,Wilko Schwarting,Guy Rosman,Sertac Karaman,Daniela Rus
Conference on Robot Learningpp.1667-1685, (2023)
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Conference on Learning for Dynamics & Control (L4DC)pp.1006-1019, (2022)
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Conference on Learning for Dynamics & Control (L4DC)pp.954-967, (2022)
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