6 accepted papers
Non-prehensile manipulation offers a robust alternative to traditional pick-and-place methods for object repositioning. However, learning such skills with dexterous, multi-fingered hands remains largely unexplored, leaving their potential for stable and efficient manipulation underutilized. Progress…
Successfully executing grasping tasks within highly cluttered spaces is still a significant hurdle in robotics, especially in scenarios involving severe target occlusion. To tackle this, we present a novel self-supervised framework driven by deep reinforcement learning that enables robots to acquire…
Vision-based tactile sensors enable high-resolution tactile perception by capturing image-based contact data. However, their utility in tactile localization is limited by their inherently small and local sensing area, as well as their dependence on distinct object surface features. We propose TacTap…
Learning real-world robotic manipulation is challenging, particularly when limited demonstrations are available. Existing methods for few-shot manipulation often rely on simulation-augmented data or pre-built modules like grasping and pose estimation, which struggle with sim-to-real gaps and lack ex…
Robots are increasingly envisioned as human companions, assisting with everyday tasks that often involve manipulating deformable objects. Recent advancements in robotic hardware and embodied AI algorithms have expanded the range of tasks robots can perform. However, current systems still struggle wi…
Magnetic-based tactile sensors (MBTS) combine the advantages of compact design and high-frequency operation but suffer from limited spatial resolution due to their sparse taxel arrays. This paper proposes SuperMag, a tactile shape reconstruction method that addresses this limitation by leveraging hi