Grasp Policy

Grasp policy research focuses on enabling robots to reliably grasp objects, a fundamental skill for manipulation. Current efforts concentrate on developing robust and generalizable policies using deep reinforcement learning, often incorporating neural networks (like transformers) and incorporating diverse data sources such as depth images and language descriptions to improve adaptability and success rates across various objects and environments. These advancements are crucial for improving robotic dexterity in diverse applications, ranging from industrial automation to assistive robotics and space exploration. Hybrid approaches combining model-based and learning-based methods are also emerging to enhance robustness and efficiency.