Grasp Prediction

Grasp prediction focuses on enabling robots to accurately determine how to grip objects, a crucial step for autonomous manipulation. Current research emphasizes developing robust and generalizable methods, often employing deep learning architectures like convolutional neural networks and transformers, sometimes integrated with prompt-based segmentation models or leveraging multimodal sensor data (RGB, thermal, LiDAR) for improved accuracy. This field is vital for advancing robotics in diverse applications, from warehouse automation and assistive robotics to autonomous driving, where reliable grasp prediction is essential for safe and efficient operation.