In this paper, a novel approach to detect incipient slip based on the contact area between a transparent silicone medium and different objects using a neuromorphic event-based vision sensor (DAVIS) is proposed. Event-based algorithms are developed to detect incipient slip, slip, stress distribution and object vibration. Thirty-seven experiments were performed on five objects with different sizes, shapes, materials and weights to compare precision and response time of the proposed approach. The proposed approach is validated by using a high speed constitutional camera (1000 FPS). The results indicate that the sensor can detect incipient slippage with an average of 44.1 ms latency in unstructured environment for various objects. It is worth mentioning that the experiments were conducted in an uncontrolled experimental environment, therefore adding high noise levels that affected results significantly. However, eleven of the experiments had a detection latency below 10 ms which shows the capability of this method. The results are very promising and show a high potential of the sensor being used for manipulation applications especially in dynamic environments.

In this paper, a novel dynamic Vision-Based Measurement method is proposed to measure contact force independent of the object sizes. A neuromorphic camera (Dynamic Vision Sensor) is utilizused to observe intensity changes within the silicone membrane where the object is in contact. Three deep Long Short-Term Memory neural networks combined with convolutional layers are developed and implemented to estimate the contact force from intensity changes over time. Thirty-five experiments are conducted using three objects with different sizes to validate the proposed approach. We demonstrate that the networks with memory gates are robust against variable contact sizes as the networks learn object sizes in the early stage of a grasp. Moreover, spatial and temporal features enable the sensor to estimate the contact force every 10 ms accurately. The results are promising with Mean Squared Error of less than 0.1 N for grasping and holding contact force using leave-one-out cross-validation method.

Event-based cameras are commonly leveraged to mitigate issues such as motion blur, low dynamic range, and limited time sampling, which plague conventional cameras. However, a lack of dedicated event-based datasets for benchmarking segmentation algorithms, especially those offering critical depth information for occluded scenes, has been observed. In response, this paper introduces a novel Event-based Segmentation Dataset (ESD), a high-quality event 3D spatial-temporal dataset designed for indoor object segmentation within cluttered environments. ESD encompasses 145 sequences featuring 14,166 manually annotated RGB frames, along with a substantial event count of 21.88 million and 20.80 million events from two stereo-configured event-based cameras. Notably, this densely annotated 3D spatial-temporal event-based segmentation benchmark for tabletop objects represents a pioneering initiative, providing event-wise depth, and annotated instance labels, in addition to corresponding RGBD frames. By releasing ESD, our aim is to offer the research community a challenging segmentation benchmark of exceptional quality.

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.