Reinforcement learning (RL) requires either manually specifying a reward function, which is
often infeasible, or learning a reward model from a large amount of human feedback, which …

This work proposes a retrieve-and-transfer framework for zero-shot robotic manipulation,
dubbed RAM, featuring generalizability across various objects, environments, 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 …

Building generalist agents that can accomplish many goals in rich open-ended
environments is one of the research frontiers for reinforcement learning. A key limiting factor …

Reward engineering has long been a challenge in Reinforcement Learning (RL) research,
as it often requires extensive human effort and iterative processes of trial-and-error to design …

Foundation models (FMs), large deep learning models pre-trained on vast, unlabeled
datasets, exhibit powerful capabilities in understanding complex patterns and generating …

Conventional state representations in reinforcement learning often omit critical task-related
details, presenting a significant challenge for value networks in establishing accurate …

Abstract human-robot collaboration (HRC) is set to transform the manufacturing paradigm by
leveraging the strengths of human flexibility and robot precision. The recent breakthrough of …

Since the 1950s, when the Turing Test was introduced, there has been notable progress in
machine language intelligence. Language modeling, crucial for AI development, has …

Long-horizon decision-making tasks present significant challenges for LLM-based agents
due to the need for extensive planning over multiple steps. In this paper, we propose a …