Zobrazeno 1 - 10
of 232
pro vyhledávání: '"Simmons Reid"'
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
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
In this work, we aim to improve transparency and efficacy in human-robot collaboration by developing machine teaching algorithms suitable for groups with varied learning capabilities. While previous approaches focused on tailored approaches for teach
Externí odkaz:
http://arxiv.org/abs/2404.15472
Demonstrations are a powerful way of increasing the transparency of AI policies. Though informative demonstrations may be selected a priori through the machine teaching paradigm, student learning may deviate from the preselected curriculum in situ. T
Externí odkaz:
http://arxiv.org/abs/2406.11850
Autor:
Zhu, Feiyu, Simmons, Reid
Large language models contain noisy general knowledge of the world, yet are hard to train or fine-tune. On the other hand cognitive architectures have excellent interpretability and are flexible to update but require a lot of manual work to instantia
Externí odkaz:
http://arxiv.org/abs/2403.00810
Autor:
Trovato Gabriele, Ramos Josue G., Azevedo Helio, Moroni Artemis, Magossi Silvia, Simmons Reid, Ishii Hiroyuki, Takanishi Atsuo
Publikováno v:
Paladyn, Vol 8, Iss 1, Pp 1-17 (2017)
The receptionist job, consisting in providing useful indications to visitors in a public office, is one possible employment of social robots. The design and the behaviour of robots expected to be integrated in human societies are crucial issues, and
Externí odkaz:
https://doaj.org/article/a658beb21bff490dbcbd2c19dfc278d5
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
Autor:
Trovato Gabriele, Ramos Josue G., Azevedo Helio, Moroni Artemis, Magossi Silvia, Simmons Reid, Ishii Hiroyuki, Takanishi Atsuo
Publikováno v:
Paladyn, Vol 10, Iss 1, Pp 436-437 (2019)
The original article was published in Paladyn, Journal of Behavioral Robotics, 2017, 8(1), 1-17, https://doi.org/10.1515/pjbr-2017-0001. The aim of this erratum is to report the results of a new analysis of the data.
Externí odkaz:
https://doaj.org/article/274525c3b4e64588b8421c5c78c9ead6
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
To collaborate well with robots, we must be able to understand their decision making. Humans naturally infer other agents' beliefs and desires by reasoning about their observable behavior in a way that resembles inverse reinforcement learning (IRL).
Externí odkaz:
http://arxiv.org/abs/2203.01855