Table tennis robots have significantly advanced in performance owing to the rapid progress in deep learning and reinforcement learning technologies. However, these advancements often require a large number of training samples. Besides, research focused on the robot serve task remains relatively limited. In response to these problems, this paper proposes a sample-efficient adjustment-learning (SEAL) method for the serve task inspired by human experience in table tennis, which can inherently augment the available training samples without the need for additional sample collection. The adjustment learning does not require complex network structures but demonstrates superior performances. The models trained by adjustment learning have good generalization and robustness, that can adapt to different serve styles and reduce system transfer errors very efficiently. In addition, the random interpolation method during dataset generation stage is introduced, and the effectiveness of simultaneous learning in both joint space and Cartesian space is also demonstrated. For specific serve task, an accuracy of less than$\mathbf{3 0 ~ m m}$to any designated position at the first shot is achieved.

In daily life, we realize various complex tasks with our two upper limbs based on the so-called bimanual coordination phenomenon. A fundamental feature of bimanual coordination is the natural tendency to synchronize the motion of two upper limbs, resulting in some preferred patterns of interlimb coordination. In this study, based on the coarse-to-fine human-robot collaboration framework, we investigate the possibility of realizing human-robot interaction and collaboration for accurate manipulation utilizing voluntary bimanual coordination. The practical motivation of utilizing the voluntary bimanual coordination, which can be perceived as an indirect way of implementing interlimb transmission of force feedback information, is to avoid the bad effect on force feedback presentation due to the counterforce if the human-robot collaboration was realized in an unimanual manner. For experimental studies, we firstly evaluated the synchronous protocols in terms of in-phase and anti-phase between two arms. Based on the studied protocol, moving target tracking was then demonstrated as an application scenario of the proposed human-robot collaboration utilizing voluntary bimanual coordination.

In this paper, we propose a robotic assistance system for carrying out a highly accurate peg-in-hole task by using a high-speed vision system and high-accuracy actuators. In this system, the vision system measures the position and the posture of the workpieces, and the actuators are moved according to this information. By using the high-speed vision system and operating the system at 500 Hz, a hole formed in one workpiece can be made to follow the complicated motion of a peg operated by a human and can be accurately aligned with the peg. In addition, since the torque required to move the hole is calculated on the basis of the position and direction data of the workpieces in the coordinate plane of the image captured by the vision system, the system is not affected by camera installation errors. Moreover, by using two cameras and combining the information from them, we made the tracking field wider. In order to evaluate the performance of the system, we executed a peg-in-hole task using a 50μm peg and a 70μm hole, and showed that the system completed the task with a success rate of 90%.

The purpose of this work is to achieve high-speed dynamic manipulation. As one example, we deal with throwing a breaking ball. In this article we deal with two types of throwing with different grasping forms. We modeled a manipulator and a ball with a sphere rolling on the tip link of a three-degrees-of-freedom manipulator in the case where a ball is thrown with spin, and with a sphere held between the tip links in the case where the ball is thrown with less spin. We simulated the motion of the ball by using these models in each case. In addition, experimental results obtained with balls thrown by a high-speed manipulator showed that the spin rates with the two types of throwing differed by a factor of 4.3 at most.

In this study, a deformation control with spatial decoupling properties is designed and implemented. This control strategy treats the shift in position and posture attributable to an external force as the deformation of the robot. The deformation dynamics are constructed from the Maxwell and Voigt models, which describe plastic and elastic deformation, respectively. Next, a control method is proposed to passively achieve different deformation characteristics in each direction. Two physical simulations with a robotic arm are executed to validate the proposed control law.

A visually guided shock absorber for high-speed catching without rebound is designed and realized. This visual shock absorber is based on the Maxwell model, which uses plastic deformation to suppress rebounding. The shock absorber is constructed from a spring-based passive elastic body and a servo control-based software damper connected in series. The visual shock absorber is realized in a robot with two degrees of freedom and high-speed vision. The robot receives the impact of a rolling object without repelling it.

This paper presents a visual compliance strategy to deal with the problem of fast peg-and-hole alignment with large position and attitude uncertainty. With the use of visual compliance and adoption of a light-weight 3-DOF active peg, decoupled alignment for position and attitude is realized. The active peg is capable of high-speed motion and with less dynamic defects than a traditional robot arm. Two high-speed cameras, one configured as eye-in-hand and the other as eye-to-hand are adopted to provide with the task-space feedback. Visual constraints for effecting the visual compliant motion are analyzed. Alignment experiments show that peg-and-hole alignment with the proposed approach could be successfully realized with robust convergence, and on average, the alignment could be realized within 0.7 s in our experimental setting.