Neuromorphic vision sensor is an attractive technology that offers high dynamic range, and low latency which are crucial in robotic applications. However, the lack of event-based data in this field, limits the sensors' performance in a real-world environments. In this paper, we propose a novel augmentation technique for neuromorphic vision sensors to improve contact force measurements from events. The proposed method shifts a proportion of events across the time domain, 'Temporal Event Shifting', to augment the dataset. A new set of grasping experiments is performed to validate and analyze the effectiveness of the proposed augmentation method for contact force measurements. The results indicate that temporal event shifting is highly effective augmentation method which improves the models' accuracy for the contact force estimation by thirty percent without performing new experiments. INDEX TERMSEvent-based augmentation, Neuromorphic augmentation, Vision-based Tactile sensor.

Object segmentation for robotic grasping under dynamic conditions often faces challenges such as occlusion, low light conditions, motion blur and object size variance. To address these challenges, we propose a Deep Learning network that fuses two types of visual signals, event-based data and RGB frame data. The proposed Bimodal SegNet network has two distinct encoders, one for each signal input and a spatial pyramidal pooling with atrous convolutions. Encoders capture rich contextual information by pooling the concatenated features at different resolutions while the decoder obtains sharp object boundaries. The evaluation of the proposed method undertakes five unique image degradation challenges including occlusion, blur, brightness, trajectory and scale variance on the Event-based Segmentation (ESD) Dataset. The evaluation results show a 6-10% segmentation accuracy improvement over state-of-the-art methods in terms of mean intersection over the union and pixel accuracy. The model code is available at https://github.com/sanket0707/Bimodal-SegNet.git