Outputs (2)

Evaluating Gaussian Grasp Maps for Generative Grasping Models (2022)
Conference Proceeding
Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2022). Evaluating Gaussian Grasp Maps for Generative Grasping Models.

Generalising robotic grasping to previously unseen objects is a key task in general robotic manipulation. The current method for training many antipodal generative grasping models rely on a binary ground truth grasp map generated from the centre thir... Read More about Evaluating Gaussian Grasp Maps for Generative Grasping Models.

Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss (2021)
Conference Proceeding
Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2021). Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss. . https://doi.org/10.1109/icpr48806.2021.9413197

In this paper we introduce two methods of improving real-time object grasping performance from monocular colour images in an end-to-end CNN architecture. The first is the addition of an auxiliary task during model training (multi-task learning). Our... Read More about Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss.