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/
LLMs have shown promising results in task planning due to their strong natural language understanding and reasoning capabilities. However, issues such as hallucinations, ambiguities in human instructions, environmental constraints, and limitations in the executing agent's capabilities often lead to
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
Policy learning focuses on devising strategies for agents in embodied artificial intelligence systems to perform optimal actions based on their perceived states. One of the key challenges in policy learning involves handling complex, long-horizon tasks that require managing extensive sequences of ac…
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…
Fairness is a critical concern in deep learning especially in healthcare where these models influence diagnoses and treatment decisions. Although fairness has been investigated in the vision-only domain the fairness of medical vision-language (VL) models remains unexplored due to the scarcity of med…
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…
This paper deals with predicting future 3D motions of 3D object scans from the previous two consecutive frames. Previous methods mostly focus on sparse motion prediction in the form of skeletons. While in this paper, we focus on predicting dense 3D motions in the form of 3D point clouds. To approach…
Deep learning models have achieved great success in supervised shape descriptor learning for 3D shape retrieval, classification, and correspondence. However, the unsupervised shape descriptor calculated via deep learning is less studied than that of supervised ones due to the design challenges of un…
Spatial cognition, as gained through the sense of vision, is one of the most important capabilities of human beings. However, for the visually impaired (VI), lack of this perceptual capability poses great challenges in their life. Therefore, we have designed Point-to-Tell-and-Touch, a wearable syste…