9 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,
Effective motion retargeting is essential for robotic hands to perform fine-grained teleoperated manipulation. However, existing methods face several key challenges: optimization-based approaches offer accurate reproduction but suffer from high computational latency; learning-based methods provide f
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
Anthropomorphic dexterous hands significantly enhance the manipulation capabilities of robots; however, balancing structural complexity with functional dexterity remains a major challenge. In this work, we propose the CasiaHand, a 15-DoF tendon-driven anthropomorphic dexterous hand featuring human-l
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 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
Object grasping in cluttered scenes is a widely investigated field of robot manipulation. Most of the current works focus on estimating grasp pose from point clouds based on an efficient single-shot grasp detection network. However, due to the lack of geometry awareness of the local grasping area, i…
Robots with a parallel-jaw gripper and suction cup is an adaptive and efficient robotic picking system. This paper proposed Policy-Oriented Instance Segmentation (POIS) for ambidextrous robots. POIS can generate a pair of target masks that allows ambidextrous robots to pick in parallel. It takes a d…