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 …

Recent efforts have incorporated large language models (LLMs) with external resources (eg,
the Internet) or internal control flows (eg, prompt chaining) for tasks requiring grounding or …

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

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 …

In supervised learning, the question of data quality and curation has been sidelined in
recent years in favor of increasingly more powerful and expressive models that can ingest …

Bayesian reward learning from demonstrations enables rigorous safety and uncertainty
analysis when performing imitation learning. However, Bayesian reward learning methods …

Designing reward functions is a challenging problem in AI and robotics. Humans usually
have a difficult time directly specifying all the desirable behaviors that a robot needs to …

In order to collaborate safely and efficiently, robots need to anticipate how their human
partners will behave. Some of today's robots model humans as if they were also robots, and …

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 …