A survey of human-in-the-loop for machine learning

X Wu, L **ao, Y Sun, J Zhang, T Ma, L He - Future Generation Computer …, 2022 - Elsevier
Abstract Machine learning has become the state-of-the-art technique for many tasks
including computer vision, natural language processing, speech processing tasks, etc …

Human-in-the-loop reinforcement learning: A survey and position on requirements, challenges, and opportunities

CO Retzlaff, S Das, C Wayllace, P Mousavi… - Journal of Artificial …, 2024 - jair.org
Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to
enable agents to learn and perform tasks autonomously with superhuman performance …

What matters in learning from offline human demonstrations for robot manipulation

A Mandlekar, D Xu, J Wong, S Nasiriany… - arxiv preprint arxiv …, 2021 - arxiv.org
Imitating human demonstrations is a promising approach to endow robots with various
manipulation capabilities. While recent advances have been made in imitation learning and …

Interactive imitation learning in robotics: A survey

C Celemin, R Pérez-Dattari, E Chisari… - … and Trends® in …, 2022 - nowpublishers.com
Interactive Imitation Learning in Robotics: A Survey Page 1 Interactive Imitation Learning in
Robotics: A Survey Page 2 Other titles in Foundations and Trends® in Robotics A Survey on …

Model-free reinforcement learning from expert demonstrations: a survey

J Ramírez, W Yu, A Perrusquía - Artificial Intelligence Review, 2022 - Springer
Reinforcement learning from expert demonstrations (RLED) is the intersection of imitation
learning with reinforcement learning that seeks to take advantage of these two learning …

Wearable EEG electronics for a Brain–AI Closed-Loop System to enhance autonomous machine decision-making

JH Shin, J Kwon, JU Kim, H Ryu, J Ok… - npj Flexible …, 2022 - nature.com
Human nonverbal communication tools are very ambiguous and difficult to transfer to
machines or artificial intelligence (AI). If the AI understands the mental state behind a user's …

Transic: Sim-to-real policy transfer by learning from online correction

Y Jiang, C Wang, R Zhang, J Wu, L Fei-Fei - arxiv preprint arxiv …, 2024 - arxiv.org
Learning in simulation and transferring the learned policy to the real world has the potential
to enable generalist robots. The key challenge of this approach is to address simulation-to …

Robot learning on the job: Human-in-the-loop autonomy and learning during deployment

H Liu, S Nasiriany, L Zhang, Z Bao… - … International Journal of …, 2022 - journals.sagepub.com
With the rapid growth of computing powers and recent advances in deep learning, we have
witnessed impressive demonstrations of novel robot capabilities in research settings …

A survey on explainable reinforcement learning: Concepts, algorithms, challenges

Y Qing, S Liu, J Song, H Wang, M Song - arxiv preprint arxiv:2211.06665, 2022 - arxiv.org
Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …

Human-in-the-loop imitation learning using remote teleoperation

A Mandlekar, D Xu, R Martín-Martín, Y Zhu… - arxiv preprint arxiv …, 2020 - arxiv.org
Imitation Learning is a promising paradigm for learning complex robot manipulation skills by
reproducing behavior from human demonstrations. However, manipulation tasks often …