3 accepted papers
Robotic dexterous grasping is important for interacting with the environment. To unleash the potential of data-driven models for dexterous grasping, a large-scale, highquality dataset is essential. While gradient-based optimization offers a promising way for constructing such datasets, previous work
Shape assembly, the process of combining parts into a complete whole, is a crucial skill for robots with broad real-world applications. Among the various assembly tasks, geometric assembly—where broken parts are reassembled into their original form (e.g., reconstructing a shattered bowl)—is particul…
Generalizable dexterous grasping is a fundamental skill for intelligent robots. To develop such skills, a large-scale, high-quality, and diverse dataset of robotic dexterous grasps—covering the GRASP taxonomy—is essential but extremely challenging to collect. Previous dexterous grasp synthesis metho…