6 accepted papers
Learning from demonstration is a powerful method for robotic skill acquisition. However, the significant expense of collecting such action-labeled robot data presents a major bottleneck. Video data, a rich data source encompassing diverse behavioral and physical knowledge, emerges as a promising alt…
In various robotic applications, understanding accurate object poses for robots is essential for high-precision tasks such as factory assembly or daily insertions. Tactile sensing, which compensates for visual information, offers rich texture-based or force-based data for object pose estimation. How
Learning from human demonstrations is an emerging trend for designing intelligent robotic systems. However, previous methods typically regard videos as instructions, simply dividing videos into action sequences for robotic repetition, which pose obstacles to generalization to diverse tasks or object
The ability to perform generalizable and precise grasping on functional object parts is a prerequisite for robotic manipulation in open environments. Recent foundation models have demonstrated promising semantic correspondence capabilities in guiding robots to grasp similar parts across objects with
Loop closure detection and pose estimation play a significant role in correcting odometry trajectories and generating globally consistent point cloud maps. Geometric feature descriptor methods neglect object-level spatial topology features, resulting in inadequate performance in loop closure detecti
Visual imitation learning (VIL) provides an efficient and intuitive strategy for robotic systems to acquire novel skills. Recent advancements in Vision Language Models (VLMs) have demonstrated remarkable performance in vision and language reasoning capabilities for VIL tasks. Despite the progress, c…