Leveraging massive knowledge and learning schemes from large language models (LLMs), recent machine learning models show notable successes in building generalist agents that exhibit the capability of general-purpose task solving in diverse domains, including natural language processing, computer vision, and robotics.
The human hand's complex kinematics allow for simultaneous grasping and manipulation of multiple objects, essential for tasks like object transfer and in-hand manipulation.
SceneDiffuser provides a unified model for solving scene-conditioned generation, optimization, and planning.
Robotic dexterous grasping is the first step to enable human-like dexterous object manipulation and thus a crucial robotic technology.
By leveraging the contact map as a hand-agnostic intermediate representation, GenDexGrasp efficiently generates diverse and plausible grasping poses with a high success rate and can transfer among diverse multi-fingered robotic hands.