In order to replace humans so as to ensure safety in highly hazardous environments, such as COVID-19 testing sites, a dual-arm humanoid robot has been developed by our NTHU team. This humanoid robot with 7-axis robotic arm, vision system, IMUtouch sensors intelligence is able to imitate human actions intelligently. There are several technologies that were developed for this humanoid robot: robotic arm joints which correspond to human arm joints, a novel multiple-object tracking technology which can distinguish individual occluded objects,a real-time eye-hand calibration technology.

We have designed a remotely-controlled robot which equipped with a 360 degree full HD camera. User’s postures are detected by measuring the accelerationrotation of changes of limb postures using inertial measurement units (IMU). User’s motion commands are wirelessly transmitted to actuate the remote robot in accordance with user’s posture. The 360 degree full scene camera keeps transmitting the real-time situation of the scene surrounding the robot back to the user, displayed on the VR headset.

"It is expected that human will work with robots in the coming years. However, it is still unknown how both can collaborate together. Meanwhile, it is still very time-consuming to deploy intelligent robots in production lines. For this work, we investigate deep comprehension for 3D (point cloud)text (voice) signals to enable novel human-robotic object referring for robotic arms. For the task

The self-powered sensing system realizes self-powered wireless wearable pressure sensing modules, self-powered intelligent environment sensing robots,self-powered smart window, which can carry out self-powered sensing signals combined with the transmission of the Internet of Things. Functions such as medical monitoring aidsautomated environmental quality sensing. In the future, this system opens a window towards automatic monitoring of environmental analysispersonal smart healthcare.

Through patented wearable sensor fusion technology, human 3D motions can be captured in real-time which allow 7-DOF robotic armdexterous robotic hand to perform anthropomorphic motiongrasping action. Integrated with eye-in-hand visual module, the adaptive gripper can intelligently decide optimal grasping strategy to ensure success grasping of arbitrary shape of objects in smart manufacturing applications.

Biomimetic adhesive has some characteristics: no residual glue, anisotropic adhesion, easy to be peeledstuckrepeated. Its adhesion could be adjusted depend on the application. The adhesive can be applied but not limited in wafer processing, movement of optoelectronic materials, 3C product assembly, daily-used fixation,producing a protective film for attaching to an object surface.

Inspired by the human nature that a child can learn by taking an objectthen observing it, we proposed two novel methods: (1) Object Detection by Interactive Perception (ODIP), where a few-shot object detector gradually learns unseen instances by interacting with a well-developed object grasping system, collecting required visual dataannotations in an automatic manner. (2) an efficienteffective few-shot object detection model with novel attention mechanism called Dual-Awareness Attention (DAnA).

The overall system first extracts embedded speaker identity features using a neural network model, then the deep neural network speech enhancement takes the augmented features as the input to generate the enhanced spectra. With the additional embedded features, the speech enhancement system can be guided to generate the optimal output corresponding to the speaker identity.

(1) Image recognition3D positioning of the pest: Modularize the system for testing on the orchard. (2) High efficiency laser pest control scanning system: It was developed to be suitable for windlessbreezy environments. (3) Enhance the economic tracked vehicle for hillside field: Self-developed power designsteering control based on dynamic model,(4) Field UGV control platform: Based on the preciseaccurate positioning in field, when robot operates on the reference path which planned by map, it can do path trackingdynamically obstacle avoiding in real-time.

This theme is designed to implement the robot＇s appearancepractical functions. Apply PSPNet to detect the walkable planeYolo to detect obstacles, so that the robot has the autonomous obstacle avoidance function, informing more information about the environmental obstacles around the visually impaired,apply CNN to locate indoor position with self-built indoor database.

”AI2 Robot City” is a game-based learning kit for primary and secondary school students, which combines AI image-recognition teaching tool of MIT App Inventor and the computational thinking board game named as "Robot City." Through this learning kit, users will learn to make smart cars, create image-recognition models, write and perform mobile application. Users will learn to write a program to recognize the personal cards in the board game, and furthermore to control the smart cars with blue-tooth and compete in the computational thinking board game.

The sweet pepper cultivationplant protection robot developed includes three parts: the intelligent image identificationpositioning system of sweet pepper diseasesinsect pests is composed of YOLO4 deep learning modelimage 3D vision integration. It can perform disease detectionspatial positioning,guide the robotic arm spraying device to accurately spray the diseased. The automatic walking system can use GPS to plan the movement path of the plant protection robot in the field, search for plants infected with pestsdiseases,perform the tasks of pest control.

Multi-axis force/torque sensors are key components of precision machinesrobot arms. Development of the multi-axis force/torque sensor is a popular research topic in Taiwan. This research developed design methods, calibration machinescalibration algorithms for multi-axis force/torque sensors. These efforts contribute the enhancement of precision of 0.20 (1.25 times than commercialize pro