Data-driven methods have been successfully applied to images from vision-based tactile sensors to fulfill various manipulation tasks. Nevertheless, these methods remain inefficient because of the lack of methods for simulating the sensors. Relevant research on simulating vision-based tactile sensors generally focus on generating images without markers, owing to the challenges in accurately generating marker motions caused by elastomer deformation. This disallows access to tactile information deducible from markers. In this letter, we propose a generative adversarial network (GAN)-based method to generate realistic marker-embedded tactile images in GelSight-like vision-based tactile sensors. We trained the proposed GAN model with an aligned real tactile and simulated depth image dataset obtained from deforming the sensor against various objects. This allows the model to translate simulated depth image sequences into RGB tactile images with markers. Furthermore, the generator in the proposed GAN allows the network to integrate the history of deformations from the depth image sequences to generate realistic marker motions during the normal and lateral sensor deformations. We evaluated and compared the positional accuracy of the markers and image similarity metrics of the images generated via our method with those from prior methods. The generated tactile images from the proposed model show a 28.3% decrease in marker positional error and a 93.5% decrease in the image similarity metric (MSE) compared to those generated by previous methods, validating the effectiveness of our approach.

Extensive research is being conducted on using robots to automate harvest. However, most of the existing research is focused on the realization of harvesting using a single robot, and there have been very few studies on harvesting and transporting crops from a smart-greenhouse perspective. In this study, we demonstrate that the work efficiency is higher when a plurality of harvesting and transporting robots are used in tandem for harvesting crops in a smart greenhouse, compared to that when a single robot is used. The harvesting and transporting speeds of these robots are modeled in accordance with the facility environment. The operating speed of the robot group comprising only the harvesting robot and the harvesting and transporting robots is derived. In addition, the derived operating speed is analyzed based on the experimental data of the developed harvesting and transporting robots, and it was found that the overall operating speed increased when an appropriate combination of harvesting and transporting robots was used.

In-hand manipulation with Cartesian-force-control-based pushing primitives is introduced to achieve the precise placement of an object in a desired position at a manufacturing site. In the bin picking process, achieving the desired grasping posture is challenging due to limitations in the sensing and control of the robotic arm, interference from clustered objects, and unintended collisions, which hinder achieving the planned pose. Even under such conditions, in cases that require precise operations, such as manufacturing processes, maintaining a desired placement posture is crucial for the precise placement of objects into the machine slot. In this paper, a pushing primitive incorporating force feedback control is applied to ensure that the gripper is consistently positioned at the edge of the grasped object regardless of the initial grasping position by utilizing the surrounding environment of the processing machine. Modeling the exact contact friction between the gripper and the grasped object is challenging; therefore, instead of relying on a motion planning approach, we addressed the problem using a control method that leverages feedback from the external force information of the robot manipulator. Additional sensors such as external cameras or tactile sensors in the gripper are not required. The pushing primitive is executed by applying a force greater than the frictional force between the gripper and the grasped object, leveraging the surrounding environment. Experimental verification confirmed that the proposed method achieves precise placement into the machine slot, regardless of initial grasping positions. It also proved to be effective on an actual testbed.

This paper proposes a new algorithm for actor-critic method using online support vector regression(SVR), which can do incremental learning and automatically track variation of environment with time-varying characteristics. It gives good generalization properties to value function approximation and helps the critic converge fast. In addition, sample vectors in data set of the online SVR are used as center positions of actor's basis functions. Actor updates policy parameters with those functions using policy gradient algorithm. Throughout simulations, the feasibility and usefulness of the proposed method is demonstrated by comparison with other methods.

In this paper, we present asymmetric pivot manipulation for picking up rigid hollow objects to achieve a hole grasp. The pivot motion, executed by a position-controlled robotic arm, enables the gripper to effectively grasp hollow objects placed horizontally such that one gripper finger is positioned inside the object’s hole, while the other contacts its outer surface along the length. Hole grasp is widely employed by humans to manipulate hollow objects, facilitating precise placement and enabling efficient subsequent operations, such as tightly packing objects into trays or accurately inserting them into narrow machine slots in manufacturing processes. Asymmetric pivoting for hole grasping is applicable to hollow objects of various sizes and hole shapes, including bottles, cups, and ducts. We investigate the variable parameters that satisfy the force balance conditions for successful grasping configurations. Our method can be implemented using a commercially available parallel-jaw gripper installed directly on a robot arm without modification. Experimental verification confirmed that hole grasp can be achieved using our proposed asymmetric pivot manipulation for various hollow objects, demonstrating a high success rate. Two use cases, namely aligning and feeding hollow cylindrical objects, were experimentally demonstrated on the testbed to clearly showcase the advantages of the hole grasp approach.