In this paper, we present a new open source dynamic quadruped robot, PADWQ (pronounced pa-dook), which features 12 torque controlled quasi direct drive joints with high control bandwidth, as well as onboard depth sensor and GPU-equipped computer that allows for a highly dynamic locomotion over uncertain terrains. In contrast to other dynamic quadruped robots based on custom actuator and machined metal structural parts, the PADWQ is entirely built from off the shelf components and standard 3D printed plastic structural parts, which allows for a rapid distribution and duplication without the need for advanced machining process. To make sure that the plastic structural parts can withstand the stress of dynamic locomotion, we performed finite element analysis (FEA) on leg structural parts as well as a continuous walking test using the physical robot, both of which the robot has passed successfully. We hope this work to help a wide range of researchers and engineers that need an affordable, highly capable and easily customizable quadruped robot.

This paper presents an autonomous grasping approach for complex-shaped objects using an anthropomorphic robotic hand. Although human-like robotic hands have a number of distinctive advantages, most of the current autonomous robotic pickup systems still use relatively simple gripper setups such as a two-finger gripper or even a suction gripper. The main difficulty of utilizing human-like robotic hands lies in the sheer complexity of the system; it is inherently tough to plan and control the motions of the high degree of freedom (DOF) system. Although data-driven approaches have been successfully used for motion planning of various robotic systems recently, it is hard to directly apply them to high-DOF systems due to the difficulty of acquiring training data. In this paper, we propose a novel approach for grasping complex-shaped objects using a high-DOF robotic manipulation system consisting of a seven-DOF manipulator and a four-fingered robotic hand with 16 DOFs. Human demonstration data are first acquired using a virtual reality controller with 6D pose tracking and individual capacitive finger sensors. Then, the 3D shape of the manipulation target object is reconstructed from multiple depth images recorded using the wrist-mounted RGBD camera. The grasping pose for the object is estimated using a residual neural network (ResNet), K-means clustering (KNN), and a point-set registration algorithm. Then, the manipulator moves to the grasping pose following the trajectory created by dynamic movement primitives (DMPs). Finally, the robot performs one of the object-specific grasping motions learned from human demonstration. The suggested system is evaluated by an official tester using five objects with promising results.