Výsledky vyhledávání - "Zhao, Michelle"

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Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback

Autor: Zhao, Michelle, Simmons, Reid, Admoni, Henny, Ramdas, Aaditya, Bajcsy, Andrea

In interactive imitation learning (IL), uncertainty quantification offers a way for the learner (i.e. robot) to contend with distribution shifts encountered during deployment by actively seeking additional feedback from an expert (i.e. human) online.

Externí odkaz: http://arxiv.org/abs/2410.08852

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Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actions

Autor: Zhao, Michelle, Simmons, Reid, Admoni, Henny, Bajcsy, Andrea

Assistive robotic arms often have more degrees-of-freedom than a human teleoperator can control with a low-dimensional input, like a joystick. To overcome this challenge, existing approaches use data-driven methods to learn a mapping from low-dimensi

Externí odkaz: http://arxiv.org/abs/2406.07767

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Multi-Agent Strategy Explanations for Human-Robot Collaboration

Autor: Pandya, Ravi, Zhao, Michelle, Liu, Changliu, Simmons, Reid, Admoni, Henny

As robots are deployed in human spaces, it is important that they are able to coordinate their actions with the people around them. Part of such coordination involves ensuring that people have a good understanding of how a robot will act in the envir

Externí odkaz: http://arxiv.org/abs/2311.11955

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Coordination with Humans via Strategy Matching

Autor: Zhao, Michelle, Simmons, Reid, Admoni, Henny

Human and robot partners increasingly need to work together to perform tasks as a team. Robots designed for such collaboration must reason about how their task-completion strategies interplay with the behavior and skills of their human team members a

Externí odkaz: http://arxiv.org/abs/2210.15099

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