We present an open-source anthropomorphic robot hand system called HRI hand. Our robot hand system was developed with a focus on the end-effector role of the collaborative robot manipulator. HRI hand is a research platform that can be built at a lower price (approximately $500, using only 3D printing) than commercial end-effectors. Moreover, it was designed as a two four-bar linkage for the under-actuated mechanism and provides pre-shaping motion similar to the human hand prior to touching an object. A URDF, python node, and rviz package is also provided to support the Robot Operating System (ROS). All hardware CAD design files and software source codes have been released and can be easily assembled and modified. The system proposed in this paper is developed with a five-finger structure, but each finger is modularized, so it can be developed with end-effectors of various shapes depending on the shape of the palm.

Hidden Markov Model (HMM) is very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications including gesture representation. Most research in this field, however, uses only HMM for recognizing simple gestures, while HMM can definitely be applied for whole gesture meaning recognition. This is very effectively applicable in Human-Robot Interaction (HRI). In this paper, we introduce an approach for HRI in which not only the human can naturally control the robot by hand gesture, but also the robot can recognize what kind of task it is executing. The main idea behind this method is the 2-stages Hidden Markov Model. The 1st HMM is to recognize the prime command-like gestures. Based on the sequence of prime gestures that are recognized from the 1st stage and which represent the whole action, the 2nd HMM plays a role in task recognition. Another contribution of this paper is that we use the output Mixed Gaussian distribution in HMM to improve the recognition rate. In the experiment, we also complete a comparison of the different number of hidden states and mixture components to obtain the optimal one, and compare to other methods to evaluate this performance.