Dynamic Hand Gesture Recognition for Robot Arm Teaching based on Improved LRCN Model In this research, we focus on finding a new method of humanrobot interaction in industrial environment. A visionbased dynamic hand gestures recognition system has been proposed for robot arm picking task. 8 dynamic hand gestures are captured for this task with a 100fps high speed camera. Based on the LRCN model, we combine the MobileNets (V2) and LSTM for this task, the MobileNets (V2) for extracting the image features and recognize the gestures, then, Long ShortTerm Memory (LSTM) architecture for interpreting the features across time steps. Around 100 samples are taken for each gesture for training at first, then, the samples are augmented to 200 samples per gesture by data augmentation. Result shows that the model is able to learn the gestures varying in duration and complexity and gestures can be recognized in 88ms with 90.62% accuracy in the experiment on our hand gesture dataset.

VisualGuided Robot Arm Using MultiTask Faster RCNN The limitation of current visual recognition methods is a big obstacle for the application of automated robot arm systems into industrial projects, which require high precision and speed. In this work, we present a Faster RCNN based multitask network, a deep neural network model, that is able to simultaneously perform three tasks including object detection, category classification and object angle estimation. Afterward, the outputs of all three tasks are utilized to decide a picking point and a rotated gripper angle for the pickandplace robot arm system. The test results show that our network achieves a mean average precision of 86.6% at IoU (Intersection over Union) of 0.7, and a mean accuracy of 83.5% for the final prediction including object localization and angle estimation. In addition, the proposed multitask network takes approximately 0.072 seconds to process an image, which is acceptable for pickandplace robot arms.