12 accepted papers
Recent advances in large language models (LLMs) have spurred interest in using these models to coordinate multi-agent robot systems. However, existing approaches often fail to handle dynamic and complex environments effectively. We present A2C-Collab, an <underline xmlns:mml="http://www.w3.org/1998/
Tool calling extends large language models (LLMs) by enabling grounded interaction with external executable interfaces, thereby supporting environment-coupled problem solving. However, mainstream in-context learning (ICL) approaches typically incorporate detailed tool documentation and usage example…
Robotic navigation plays a pivotal role in a wide range of real-world applications. While traditional navigation systems focus on efficiency and obstacle avoidance, their inability to model complex human behaviors in shared spaces has underscored the growing need for socially aware navigation. In th
Spatial cognition refers to the ability to gain knowledge about their surroundings and utilize this information to identify their location, acquire resources, and navigate their way back to familiar places. People with blindness and low vision (pBLV) face significant challenges with spatial cognitio…
3D Embodied Spatial Reasoning, emphasizing an agent’s interaction with its surroundings for spatial information inference, is adeptly facilitated by the process of Situated Question Answering in 3D Scenes (SQA3D). SQA3D requires an agent to comprehend its position and orientation within a 3D scene b…
Zero-Shot Object Goal Navigation (ZS-OGN) enables robots to navigate toward objects of unseen categories without prior training. Traditional approaches often leverage categorical semantic information for navigation guidance, which struggles when only partial objects are observed or detailed and func…
3D scene semantic segmentation plays a crucial role in robotics by enabling robots to understand and interpret their environment in a detailed and context-aware manner, facilitating tasks such as navigation, object manipulation, and interaction within complex spaces. A preponderance of methodology p…
Current methods for large-scale point cloud scene semantic segmentation rely on manually annotated dense point-wise labels, which are costly, labor-intensive, and prone to errors. Consequently, gathering point cloud scenes with billions of labeled points is impractical in real-world scenarios. In th…
Object detection is a fundamental task for autonomous driving, which aim to identify and localize objects within an image. Deep learning has made great strides for object detection, with popular models including Faster R-CNN, YOLO, and SSD. The detection accuracy and computational cost of object det
"Recently, Vision-Language Pre-training (VLP) techniques have greatly benefited various vision-language tasks by jointly learning visual and textual representations, which intuitively helps in Optical Character Recognition (OCR) tasks due to the rich visual and textual information in scene text imag…
Keyword search is a fundamental task to retrieve information that is the most relevant to the query keywords. Keyword search over graphs aims to find subtrees or subgraphs containing all query keywords ranked according to some criteria. Existing studies all assume that the graphs have complete infor…
Predicting the link between two nodes is a fundamental problem for graph data analytics. In attributed graphs, both the structure and attribute information can be utilized for link prediction. Most existing studies focus on transductive link prediction where both nodes are already in the graph. Howe…