Large Language Models (LLMs) equipped with extensive world knowledge and strong
reasoning skills can tackle diverse tasks across domains, often by posing them as …

Autonomous robotic systems capable of learning novel manipulation tasks are poised to
transform industries from manufacturing to service automation. However, modern methods …

This survey presents an overview of methods for learning from video (LfV) in the context of
reinforcement learning (RL) and robotics. We focus on methods capable of scaling to large …

Learning a universal policy across different robot morphologies can significantly improve
learning efficiency and enable zero-shot generalization to unseen morphologies. However …

Background: Simulator-based driving assessments (SA) have recently been used and
studied for various purposes, particularly for post-stroke patients. Automating such …

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 …

Large Language Models (LLMs), known to contain a strong awareness of world knowledge,
have allowed recent approaches to achieve excellent performance on Long-Video …

Robotic grasping is a fundamental aspect of robot functionality, defining how robots interact
with objects. Despite substantial progress, its generalizability to counter-intuitive or long …

With large language models, robots can understand language more flexibly and more
capable than ever before. This survey reviews recent literature and situates it into a …

Despite the significant advancements in Deep Reinforcement Learning (RL) observed in the
last decade, the amount of training experience necessary to learn effective policies remains …