Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …

Legged locomotion holds the premise of universal mobility, a critical capability for many real-
world robotic applications. Both model-based and learning-based approaches have …

Large language models (LLMs) are currently at the forefront of intertwining AI systems with
human communication and everyday life. Due to rapid technological advances and their …

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 …

Since the emergence of ChatGPT, research on large language models (LLMs) has actively
progressed across various fields. LLMs, pre-trained on vast text datasets, have exhibited …

The recent introduction of large language models (LLMs) has revolutionized the field of
robotics by enabling contextual reasoning and intuitive human-robot interaction in domains …

The realization of universal robots is an ultimate goal of researchers. However, a key hurdle
in achieving this goal lies in the robots' ability to manipulate objects in their unstructured …

Robotic manipulation in open-world settings requires not only task execution but also the
ability to detect and learn from failures. While recent advances in vision-language models …

Parkour poses a significant challenge for legged robots, requiring navigation through
complex environments with agility and precision based on limited sensory inputs. In this …

Curriculum learning is a training mechanism in reinforcement learning (RL) that facilitates
the achievement of complex policies by progressively increasing the task difficulty during …