4 accepted papers
Humans instinctively adjust their viewpoints to resolve occlusions and infer spatial relationships, enabling effective perception and navigation in cluttered environments. This capability, however, remains a significant challenge for robotic systems. To address this, we propose GPD-AP, a novel activ…
Graph Foundation Models (GFMs) have achieved remarkable success in generalizing across diverse domains. However, they mainly focus on Text-Attributed Graphs (TAGs), leaving Multimodal-Attributed Graphs (MAGs) largely untapped. Developing Multimodal Graph Foundation Models (MGFMs) allows for leveragi…
Offline reinforcement learning strives to enable agents to effectively utilize pre-collected offline datasets for learning. Such an offline setup tremendously mitigates the problems of online reinforcement learning algorithms in real-world applications, particularly in scenarios where interactions a
Accurate object 6D pose estimation is a core task for robot bin-picking applications, especially when objects are randomly stacked with heavy occlusion. To address this problem, this paper proposes a simple but novel Point-wise Pose Regression Network (PPR-Net). For each point in the point cloud, th…