3 accepted papers
Large language models (LLMs) have demonstrated remarkable reasoning capabilities. However, in real-world robotic tasks, LLMs face grounding issues and lack precise feedback, resulting in the generated solutions deviating from the actual situation. In this paper, we propose Double-Feedback, a method
Panoptic 3D reconstruction from a monocular video is a fundamental perceptual task in robotic scene understanding. However, existing efforts suffer from inefficiency in terms of inference speed and accuracy, limiting their practical applicability. We present EPRecon, an efficient real-time panoptic
To complete tasks in dynamic environments, robots need to timely update their plans to react to environment changes. Traditional stripe-like or learning-based planners struggle to achieve this due to their high reliance on meticulously predefined planning rules or labeled data. Fortunately, recent w