Evolvinggrasp: Evolutionary Grasp Generation Via Efficient Preference Alignment

Dexterous robotic hands often struggle to generalize effectively in complex environments due to models trained on low-diversity data. However, the real world presents an inherently unbounded range of scenarios. A natural solution is to enable robots learning from experience in complex environments-an approach akin to evolution, where systems improve through learning from both failures and successes. Motivated by this, we propose EvolvingGrasp, an evolutionary grasp generation method that continuously enhances grasping performance through efficient preference alignment. Specifically, we introduce Handpose-wise