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 …

Today's robot policies exhibit subpar performance when faced with the challenge of
generalizing to novel environments. Human corrective feedback is a crucial form of …

Large language models (LLMs) pre-trained on vast internet-scale data have showcased
remarkable capabilities across diverse domains. Recently, there has been escalating …

Robot models, particularly those trained with large amounts of data, have recently shown a
plethora of real-world manipulation and navigation capabilities. Several independent efforts …

In recent years, reinforcement learning and imitation learning have shown great potential for
controlling humanoid robots' motion. However, these methods typically create simulation …

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 …

Autonomous robot navigation and manipulation in open environments require reasoning
and replanning with closed-loop feedback. We present COME-robot, the first closed-loop …

A general-purpose service robot (GPSR), which can execute diverse tasks in various
environments, requires a system with high generalizability and adaptability to tasks and …

The convergence of embodied agents and large language models (LLMs) has brought
significant advancements to embodied instruction following. Particularly, the strong …

The ability to reflect on and correct failures is crucial for robotic systems to interact stably with
real-life objects. Observing the generalization and reasoning capabilities of Multimodal …