6 accepted papers
Vision-Language Models (VLMs) have demonstrated significant potential for end-to-end autonomous driving, yet a substantial gap remains between their current capabilities and the reliability necessary for real-world deployment. A critical challenge is their fragility, characterized by hallucinations …
Active vision learning aims to develop intelligent systems capable of actively exploring and understanding their surroundings to optimize detection performance. Although current research has begun to explore how reinforcement learning can drive robots to actively perceive their environment, it often…
Perception and interaction with articulated objects present a unique challenge for service robots. Although recent research has emphasized understanding articulated shapes and affordance proposals, existing methods only address isolated aspects, failing to develop comprehensive strategies for roboti…
Masked Autoencoders (MAE) demonstrated exceptional performance in natural language processing and 2D vision tasks and have now been introduced into point cloud representation learning. We propose Point-UMAE, a novel self-supervised learning method based on a Unet-like structure, designed to enhance…
Precise perception of articulated objects is vital for empowering service robots. Recent studies mainly focus on point cloud, a single-modal approach, often neglecting vital texture and lighting details and assuming ideal conditions like optimal viewpoints, unrepresentative of real-world scenarios.…
Graph neural networks (GNNs), as the de-facto model class for representation learning on graphs, are built upon the multi-layer perceptrons (MLP) architecture with additional message passing layers to allow features to flow across nodes. While conventional wisdom commonly attributes the success of G…