Reference adaptation for robots in physical interactions with unknown environments

In this paper, we propose a method of reference adaptation for robots in physical interactions with unknown environments. A cost function is constructed to describe the interaction performance, which combines trajectory tracking error and interaction force between the robot and the environment. It is minimized by the proposed reference adaptation based on trajectory parametrization and iterative learning. An adaptive impedance control is developed to make the robot be governed by the target impedance model. Simulation and experiment studies are conducted to verify the effectiveness of the proposed method

ING2 (inhibitor of growth protein-2) plays a crucial role in preimplantation development

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Adaptive control for robot navigation in human environments based on social force model

In this paper, we introduce a novel control scheme based on the social force model for robots navigating in human environments. Social proxemics potential field is constructed based on the theory of proxemics and used to generate social interaction force for design of robot motion control. A combined kinematic/dynamic control is proposed to make the robot follow the target social force model, in the presence of kinematic velocity constraints. Under the proposed framework, given a specific social convention, robot is able to generate and modify its path smoothly without violating the proxemics constraints. The validity of the proposed method is verified through experimental studies using the V-rep platform

Optimal critic learning for robot control in time-varying environments

In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q-function based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. Simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified

Continuous critic learning for robot control in physical human-robot interaction

In this paper, optimal impedance adaptation is investigated for interaction control in constrained motion. The external environment is modeled as a linear system with parameter matrices completely unknown and continuous critic learning is adopted for interaction control. The desired impedance is obtained which leads to an optimal realization of the trajectory tracking and force regulation. As no particular system information is required in the whole process, the proposed interaction control provides a feasible solution to a large number of applications. The validity of the proposed method is verified through simulation studies

Adaptive optimal control for linear discrete time-varying systems

In this paper, adaptive optimal control is proposed for time-varying discrete linear system subject to unknown system dynamics. The idea of the method is a direct application of the Q-learning adaptive dynamic programming for time-varying system. In order to derive the optimal control policy, a actor-critic structure is constructed and time-varying least square method is adopted for parameter adaptation. It has shown that the derived control policy can robustly stabilize the time varying system and guarantee an optimal control performance at the same time. As no particular system information is required throughout the process, the proposed techniques provide a potential feasible solution to a large variety of control application. The validity of the proposed method is verified through simulation studies

CAM/CAD Point Cloud Part Segmentation via Few-Shot Learning

3D part segmentation is an essential step in advanced CAM/CAD workflow. Precise 3D segmentation contributes to lower defective rate of work-pieces produced by the manufacturing equipment (such as computer controlled CNCs), thereby improving work efficiency and attaining the attendant economic benefits. A large class of existing works on 3D model segmentation are mostly based on fully-supervised learning, which trains the AI models with large, annotated datasets. However, the disadvantage is that the resulting models from the fully-supervised learning methodology are highly reliant on the completeness of the available dataset, and its generalization ability is relatively poor to new unknown segmentation types (i.e. further additional novel classes). In this work, we propose and develop a noteworthy few-shot learning-based approach for effective part segmentation in CAM/CAD; and this is designed to significantly enhance its generalization ability and flexibly adapt to new segmentation tasks by using only relatively rather few samples. As a result, it not only reduces the requirements for the usually unattainable and exhaustive completeness of supervision datasets, but also improves the flexibility for real-world applications. As further improvement and innovation, we additionally adopt the transform net and the center loss block in the network. These characteristics serve to improve the comprehension for 3D features of the various possible instances of the whole work-piece and ensure the close distribution of the same class in feature space.Comment: 7 pages, 5 figure