14 accepted papers
Language-guided robotic grasping in cluttered environments presents significant challenges due to severe occlusions and complex scene structures, which often hinder accurate target localization. Existing approaches typically suffer from limited observational capabilities, resulting in suboptimal exp…
Hierarchical Vision-Language-Action (VLA) models have rapidly become a dominant paradigm for robotic manipulation. It typically comprising a Vision-Language backbone for perception and understanding, together with a generative policy for action generation. However, its performance is increasingly bo
Recent advances in multimodal large language models (MLLMs) have opened new opportunities for embodied intelligence, enabling multimodal understanding, reasoning, and interaction, as well as continuous spatial decision-making. Nevertheless, current MLLM-based embodied systems face two critical limit…
Language-guided robotic grasping in cluttered environments presents significant challenges due to severe occlusions and complex scene structures, which often hinder accurate target localization. Existing approaches typically suffer from limited observational capabilities, resulting in suboptimal exp
Visual object navigation, requiring agents to locate target objects in novel environments through egocentric visual observation, remains a critical challenge in Embodied AI. We propose FEG-VON, a training-free framework that constructs and maintains a Frontier Embedding Graph for efficient Visual Ob
Dynamic grasping of moving objects in complex, continuous motion scenarios remains challenging. Reinforcement Learning (RL) has been applied in various robotic manipulation tasks, benefiting from its closed-loop property. However, existing RL-based methods do not fully explore the potential for enha
Robotic grasping, serving as the cornerstone of robot manipulation, is fundamental for embodied intelligence. Manipulation in challenging scenarios demands grasp detection algorithms with higher efficiency and generalizability. However, for general 6-Dof grasp detection, most data-driven methods dir
Traditional affordance segmentation on 3D point cloud objects requires massive amounts of annotated training data and can only make predictions within predefined classes and affordance tasks. To overcome these limitations, we propose a variation-robust few-shot 3D affordance segmentation network (VR
This paper presents GenH2R a framework for learning generalizable vision-based human-to-robot (H2R) handover skills. The goal is to equip robots with the ability to reliably receive objects with unseen geometry handed over by humans in various complex trajectories. We acquire such generalizability b…
A series of region-based methods succeed in extracting regional features and enhancing grasp detection quality. However, faced with a cluttered scene with potential collision, the definition of the grasp-relevant region stays inconsistent. In this paper, we propose Normalized Grasp Space (NGS) from…
Fast and robust object grasping in clutter is a crucial component of robotics. Most current works resort to the whole observed point cloud for 6-Dof grasp generation, ignoring the guidance information excavated from global semantics, thus limiting high-quality grasp generation and real-time performa
Generalizable manipulation skills, which can be composed to tackle long-horizon and complex daily chores, are one of the cornerstones of Embodied AI. However, existing benchmarks, mostly composed of a suite of simulatable environments, are insufficient to push cutting-edge research works because the…
Manipulating unseen articulated objects through visual feedback is a critical but challenging task for real robots. Existing learning-based solutions mainly focus on visual affordance learning or other pre-trained visual models to guide manipulation policies, which face challenges for novel instance
Hand pose estimation is important for human computer interaction, but the performance is not satisfying when the hand is interacting with objects. To alleviate the influence of unknown objects, we propose a novel weakly supervised segmentation guided scheme to estimate hand poses. Approximate hand m…