AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, so do risks from misalignment. To provide a comprehensive …

Researchers in human–robot collaboration have extensively studied methods for inferring
human intentions and predicting their actions, as this is an important precursor for robots to …

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 …

Building a robot that can understand and learn to interact by watching humans has inspired
several vision problems. However, despite some successful results on static datasets, it …

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 …

Language provides a way to break down complex concepts into digestible pieces. Recent
works in robot imitation learning use language-conditioned policies that predict actions …

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 …

With the advancements in human–robot interaction (HRI), robots are now capable of
operating in close proximity and engaging in physical interactions with humans (pHRI) …

Humans often demonstrate diverse behaviors due to their personal preferences, for
instance, related to their individual execution style or personal margin for safety. In this …

For robots to seamlessly interact with humans, we first need to make sure that humans and
robots understand one another. Diverse algorithms have been developed to enable robots …