The proliferation of Large Language Models (LLMs) has s fueled a shift in robot learning
from automation towards general embodied Artificial Intelligence (AI). Adopting foundation …

Language-conditioned robotic manipulation represents a cutting-edge area of research,
enabling seamless communication and cooperation between humans and robotic agents …

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 …

This paper presents GenH2R a framework for learning generalizable vision-based human-to-
robot (H2R) handover skills. The goal is to equip robots with the ability to reliably receive …

Rapid progress in high-level task planning and code generation for open-world robot
manipulation has been witnessed in Embodied AI. However, previous studies put much …

Scalable annotation approaches are crucial for constructing extensive 3D-text datasets,
facilitating a broader range of applications. However, existing methods sometimes lead to …

Learning reward functions remains the bottleneck to equip a robot with a broad repertoire of
skills. Large Language Models (LLM) contain valuable task-related knowledge that can …

The rapid progress of foundation models has led to the prosperity of autonomous agents,
which leverage the universal capabilities of foundation models to conduct reasoning …

Grounding the reasoning ability of large language models (LLMs) for embodied tasks is
challenging due to the complexity of the physical world. Especially, LLM planning for multi …

Today, the Metaverse consists of various platforms, including digital twins of the physical
world as well as virtual and blended digital-material environments that offer immersive …