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 robotic assembly system for Soma cube blocks, which, after observing the individual blocks and their assembled shape, quickly plans and executes the assembly motion sequence that picks up each block and incrementally build the target shape. A multi stage planner is used to find the suitable assembly solutions, assembly sequences and grip sequences considering various constraints, and re-grasping is used when the block target pose is not directly realizable or the block pose is ambiguous. The suggested system is implemented for a commercial UR5e robotic arm and a novel two degrees of freedom (DOF) gripper capable of in-hand manipulation, which further speeds up the manipulation speed. It was experimentally validated through a public competitive demonstration, where the suggested system completed all assembly tasks reliably with outstanding performance.

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.

In recent years, the field of robotic portrait drawing has garnered considerable interest, as evidenced by the growing number of researchers focusing on either the speed or quality of the output drawing. However, the pursuit of either speed or quality alone has resulted in a trade-off between the two objectives. Therefore, in this paper, we propose a new approach that combines both objectives by leveraging advanced machine learning techniques and a variable line width Chinese calligraphy pen. Our proposed system emulates the human drawing process, which entails planning the sketch and creating it on the canvas, thus providing a realistic and high-quality output. One of the main challenges in portrait drawing is preserving the facial features, such as the eyes, mouth, nose, and hair, which are crucial for capturing the essence of a person. To overcome this challenge, we employ CycleGAN, a powerful technique that retains important facial details while transferring the visualized sketch onto the canvas. Moreover, we introduce the Drawing Motion Generation and Robot Motion Control Modules to transfer the visualized sketch onto a physical canvas. These modules enable our system to create high-quality portraits within seconds, surpassing existing methods in terms of both time efficiency and detail quality. Our proposed system was evaluated through extensive real-life experiments and showcased at the RoboWorld 2022 exhibition. During the exhibition, our system drew portraits of more than 40 visitors, yielding a survey outcome with a satisfaction rate of 95%. This result indicates the effectiveness of our approach in creating high-quality portraits that are not only visually pleasing but also accurate.

As gestures play an important role in human communication, there have been a number of service robots equipped with a pair of human-like arms for gesture-based human–robot interactions. However, the arms of most human companion robots are limited to slow and simple gestures due to the low maximum velocity of the arm actuators. In this work, we present the JF-2 robot, a mobile home service robot equipped with a pair of torque-controlled anthropomorphic arms. Thanks to the low inertia design of the arm, responsive Quasi-Direct Drive (QDD) actuators, and active compliant control of the joints, the robot can replicate fast human dance motions while being safe in the environment. In addition to the JF-2 robot, we also present the JF-mini robot, a scaled-down, low-cost version of the JF-2 robot mainly targeted for commercial use at kindergarten and childcare facilities. The suggested system is validated by performing three experiments, a safety test, teaching children how to dance along to the music, and bringing a requested item to a human subject.