Early research on physical human–robot interaction (pHRI) has necessarily focused on
device design—the creation of compliant and sensorized hardware, such as exoskeletons …

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems
to align with human goals. RLHF has emerged as the central method used to finetune state …

Reward specification is a notoriously difficult problem in reinforcement learning, requiring
extensive expert supervision to design robust reward functions. Imitation learning (IL) …

While reinforcement learning (RL) has become a more popular approach for robotics,
designing sufficiently informative reward functions for complex tasks has proven to be …

Reward functions are difficult to design and often hard to align with human intent. Preference-
based Reinforcement Learning (RL) algorithms address these problems by learning reward …

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 …

Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

Customizing robotic behaviors to be aligned with diverse human preferences is an
underexplored challenge in the field of embodied AI. In this paper we present Promptable …

Recent successes aside, reinforcement learning (RL) still faces significant challenges in its
application to the real-world robotics domain. Guiding the learning process with additional …

Designing reward functions is a difficult task in AI and robotics. The complex task of directly
specifying all the desirable behaviors a robot needs to optimize often proves challenging for …