Outputs (2)

Evaluating Gaussian Grasp Maps for Generative Grasping Models (2022)
Presentation / Conference Contribution
Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2022, July). Evaluating Gaussian Grasp Maps for Generative Grasping Models. Presented at Proc. Int. Joint Conf. Neural Networks, Padova, Italy

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)
Presentation / Conference Contribution
Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2021, January). Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy

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.