Large Language Models (LLMs) have shown exceptional generative abilities in various
natural language and generation tasks. However, possible anthropomorphization and …

Preference Based Reinforcement Learning has shown much promise for utilizing human
binary feedback on queried trajectory pairs to recover the underlying reward model of the …

Handling trust is one of the core requirements for facilitating effective interaction between the
human and the AI agent. Thus, any decision-making framework designed to work with …

Preference-based Reinforcement Learning (PbRL) methods utilize binary feedback from the
human in the loop (HiL) over queried trajectory pairs to learn a reward model in an attempt …

Reinforcement Learning has suffered from poor reward specification, and issues for reward
hacking even in simple enough domains. Preference Based Reinforcement Learning …

Human-in-the-loop (HiL) reinforcement learning is gaining traction in domains with large
action and state spaces, and sparse rewards by allowing the agent to take advice from HiL …

The growing presence of AI-driven systems in everyday life calls for the development of
efficient methods to facilitate interactions between humans and AI agents. At the heart of …

Trust between team members is a necessary part of any successful cooperation. Therefore,
in mixed human-robot teams, the robot must possess the ability to model, assess and …