The strength of the human hand lies in its ability to manipulate objects precisely and robustly. In contrast, simple robotic grippers have low dexterity and fail to handle small objects effectively. This is why many automation tasks remain unsolved by robots. This paper presents an optimization-based framework for in-hand manipulation with a robotic hand equipped with compact Magnetic Tactile Sensors (MTSs). We formulate a trajectory optimization problem using Nonlinear Programming (NLP) for finger movements while ensuring contact points to change along the geometry of the fingers. Using the optimized trajectory from the solver, we implement and test an open-loop controller for rolling motion. To further enhance robustness and accuracy, we introduce a force controller for the fingers and a state estimator for the object utilizing MTSs. The proposed framework is validated through comparative experiments, showing that incorporating the force control with compliance consideration improves the accuracy and robustness of the rolling motion. Rolling an object with the force controller is 30% more likely to succeed than running an open-loop controller. The demonstration video is available at https://youtu.be/6J_muL_AyE8.

Calibration of fixtures in robotic work cells is essential but also time consuming and error-prone, and poor calibration can easily lead to wasted debugging time in down-stream tasks. Contact-based calibration methods let the user measure points on the fixture’s surface with a tool tip attached to the robot’s end effector. Most such methods require the user to manually annotate correspondences on the CAD model, however, this is error-prone and a cumbersome user experience. We propose a correspondence-free alternative: The user simply measures a few points from the fixture’s surface, and our method provides a tight superset of the poses which could explain the measured points. This naturally detects ambiguities related to symmetry and uninformative points and conveys this uncertainty to the user. Perhaps more importantly, it provides guaranteed bounds on the pose. The computation of such bounds is made tractable by the use of a hierarchical grid on SE(3). Our method is evaluated both in simulation and on a real collaborative robot, showing great potential for easier and less error-prone fixture calibration. sites.google.com/view/ttpose

This paper presents a method for contact-based pose estimation of workpieces using a collaborative robot. The proposed pose estimation exploits positions and surface normal vectors along an arbitrary path on an object with known geometry, where surface normal vectors are estimated based on contact forces measured by the robot. When data is only available along a single path, it is difficult to find initial correspondences between source data (recorded points and normal vectors) and target data (CAD of an object); hence, a novel weighted incremental spatial search approach for generating correspondences based on point pair features is proposed. Subsequently, robust pose estimation is employed to reduce the effect of erroneous correspondences. The proposed pose estimation is verified in simulation on three paths on two objects and with different levels of noise on the source data to quantify the robustness of the algorithm. Finally, the method is experimentally validated to provide an average pose rotation and translation accuracy of$\mathbf{0.55}^{\circ}$and 0.51 mm, respectively, when using the robust estimation cost function Geman-McClure.

This paper presents a framework for transferring surface finishing skills to new surface geometries while preserving the surface finish quality. The main idea is to estimate the contact area between the workpiece and the tool by using 3D point cloud approach and replicate a given material removal rate and the accumulated material removal, as these quantities are the main parameters for quality. The grinding motion trajectory is generated by solving a constrained optimization problem that minimizes the maximal point-wise deviation between actual and desired material removal and simultaneously minimizes the average deviation between actual and desired material removal rate. The proposed approach is verified in simulation to show the difference between direct replication of force/motion and the proposed replication of material removal. Finally, experimental results confirm that the quality of a surface finishing task can be transferred to new surface geometries with the proposed method.

Humans play sports to improve their athletic ability. The robot, especially humanoid robot, is also able to improve its athletic performances, such as reaction speed and balancing, through robot sports. Therefore, robots have been developed through performing various robot sports events such as robot soccer, robot marathon, robot fight and so on. In this reason, The Ski Robot Challenge was held in South Korea in commemoration of the PyeongChang 2018 Winter Olympic Games. The event was an Alpine slalom skiing competition in the almost same rules to human's but on a relatively short course (80m). To participate in this ski tournament, the skiing robot DIANA has been developed. In this video, the skiing robot technologies were introduced. At first, she must be able to recognize the flags. The deep learning method was used to recognize them. Secondly, she had a motion pattern to perform the carving turn, the most difficult and fastest skiing technique. In order to improve the stability, she compensated her motion to follow reference COP, based on the measured F/T sensor data. In addition, IMU sensor was used to remove instantaneous disturbance. Using these methods, the humanoid robot, DIANA, that can perform the carved turn on a realworld slope was successfully developed.