Designing a robot with the best accuracy is always an attractive research direction in the robotics community. In order to create a Gough–Stewart platform with guaranteed accuracy performance for a dedicated controller, this paper describes a novel advanced optimal design methodology: control-based design methodology. This advanced optimal design method considers the controller positioning accuracy in the design process for getting the optimal geometric parameters of the robot. In this paper, three types of visual servoing controllers are applied to control the motions of the Gough–Stewart platform: leg-direction-based visual servoing, line-based visual servoing, and image moment visual servoing. Depending on these controllers, the positioning error models considering the camera observation error together with the controller singularities are analyzed. In the next step, the optimization problems are formulated in order to get the optimal geometric parameters of the robot and the placement of the camera for the Gough–Stewart platform for each type of controller. Then, we perform co-simulations on the three optimized Gough–Stewart platforms in order to test the positioning accuracy and the robustness with respect to the manufacturing errors. It turns out that the optimal control-based design methodology helps get both the optimum design parameters of the robot and the performance of the controller {robot + dedicated controller}.

In this article, an adaptive vision-admittance-based leakage-type (LT) sliding mode control (SMC) is proposed to solve the uncertain robots’ compliant force control problem with actuator saturation. First, we design the vision-admittance-based model to generate an online reference trajectory concerning the predefined task trajectory and the interaction force command in the image feature space. Second, an adaptive SMC (ASMC) is created to track the generated reference image feature trajectory with high precision. An LT adaptive control law is applied in this controller to overcome the system uncertainties’ impacts in the situation where the upper bound of uncertainty is unknown. Moreover, an auxiliary dynamic term is taken into account in this controller’s design to solve the saturation nonlinearity problem. The feature tracking error can be guaranteed to be convergent according to the Lyapunov stability analysis. Finally, prototype experiments are conducted on a 6-degree-of-freedom (DOF) STEP SD7/700 robot to validate the superiority of the proposed control methodology.

Task-driven Language Modeling (TLM) can reduce the amount of calculations by an order of magnitude in NLP tasks but achieve the effect of matching the BERT-like large model. It uses BM25 to retrieve relevant data subset from general datasets and combine them with task dataset for joint training, but the extraction process does not consider semantic information, and IRGAN can further extracts semantic information features in the way of adversarial training, and the combination of them can use semantic information to enhance the relevance between the auxiliary training dataset and the labeled task dataset, and then through linearly decreasing joint training in for low-resource tasks have been improved compared to TLM.