8 accepted papers
Human-to-robot handover is a fundamental capability in human-robot interaction, critical for effective collaboration in service and assistive domains. Despite recent progress, ensuring both reliability and safety-particularly collision-free interaction with the human hand-remains a major challenge,
Dexterous functional tool-use grasping is essential for effective robotic manipulation of tools. However, existing approaches face significant challenges in efficiently constructing large-scale datasets and ensuring generalizability to everyday object scales. These issues primarily arise from size m
Grasp is at the core of robotic manipulation tasks. Nonetheless, most 6-DOF methods resort to a one-time setup via intensive analytics and targeting a predetermined domain. On the other hand, learning and adapting in real environments is of great promise to robotics yet challenging. In this context,
Teleoperation offers the possibility of enabling robots to replace humans in operating within hazardous environments. While it provides greater adaptability to unstructured settings than full autonomy, it also imposes significant burdens on human operators, leading to operational errors. To address
Robotic grasping guided by natural language instructions faces challenges due to ambiguities in object descriptions and the need to interpret complex spatial context. Existing visual grounding methods often rely on datasets that fail to capture these complexities, particularly when object categories
Grasping is one of the most fundamental skills for humans to interact with objects. However, it remains a challenging problem for anthropomorphic hands, due to the lack of object affordance understanding and high-dimensional grasp planning. In this work, we propose an anthropomorphic hand grasping f…
Grasping with anthropomorphic robotic hands involves much more hand-object interactions compared to parallel-jaw grippers. Modeling hand-object interactions is essential to the study of multi-finger hand dextrous manipulation. This work presents DVGG, an efficient grasp generation network that takes
Grasping in cluttered environments is one of the most fundamental skills in robotic manipulation. Most of the current works focus on estimating grasp poses for parallel-jaw or suction-cup end effectors. However, the study for dexterous anthropomorphic hand grasping in clutter remains a great challen…