Tangram assembly, the art of human intelligence and manipulation dexterity, is a new challenge for robotics and reveals the limitations of state-of-the-arts. Here, we describe our initial exploration and highlight key problems in reasoning, planning, and manipulation for robotic tangram assembly. We present MRChaos (Master Rules from Chaos), a robust and general solution for learning assembly policies that can generalize to novel objects. In contrast to conventional methods based on prior geometric and kinematic models, MRChaos learns to assemble randomly generated objects through self-exploration in simulation without prior experience in assembling target objects. The reward signal is obtained from the visual observation change without manually designed models or annotations. MRChaos retains its robustness in assembling various novel tangram objects that have never been encountered during training, with only silhouette prompts. We show the potential of MRChaos in wider applications such as cutlery combinations. The presented work indicates that radical generalization in robotic assembly can be achieved by learning in much simpler domains. The code will be available https://robotll.github.io/MasterRulesFromChaos/.

Inspired by the immune-endocrine system, an improved biological comprehensive optimization algorithm (IBCOA) is proposed for industrial big data analysis and cloud manufacturing service matching. IBCOA employs a dual-level strategy: a bottom-level global optimization immune algorithm (GOIA) narrows down the search space to optimize short-term parameters, while a top-level fuzzy weighted comprehensive evaluation (FWCE) refines the solutions by incorporating long-term performance metrics. Experimental results demonstrate IBCOA’s superior performance, showing higher accuracy, recall, and F1 scores compared to least squares, decision trees, andK-means clustering, along with longer execution time and lower error rates. When tested on standard benchmarks including Iris (classification), MNIST (handwritten digits), and CIFAR-10 (image recognition), IBCOA achieves remarkable accuracies, highlighting its strong generalization and adaptability. The algorithm not only addresses immediate production requirements in polyester fiber industrial data analysis but also enhances long-term operational efficiency and product quality. By balancing stakeholder interests (suppliers, consumers, operators), it promotes sustainable development on industrial internet platforms. This work provides a robust solution for the industrial Internet of Things (IIoT) service evaluation and classification tasks, demonstrating transformative potential for cloud manufacturing resource allocation across diverse applications.

This paper tackles the task of singulating and grasping paper-like deformable objects. We refer to such tasks as paper-flipping. In contrast to manipulating deformable objects that lack compression strength (such as shirts and ropes), minor variations in the physical properties of the paper-like deformable objects significantly impact the results, making manipulation highly challenging. Here, we present Flipbot, a novel solution for flipping paper-like deformable objects. Flipbot allows the robot to capture object physical properties by integrating exteroceptive and proprioceptive perceptions that are indispensable for manipulating deformable objects. Furthermore, by incorporating a proposed coarse-to-fine exploration process, the system is capable of learning the optimal control parameters for effective paper-flipping through proprioceptive and exteroceptive inputs. We deploy our method on a real-world robot with a soft gripper and learn in a self-supervised manner. The resulting policy demonstrates the effectiveness of Flipbot on paper-flipping tasks with various settings beyond the reach of prior studies, including but not limited to flipping pages throughout a book and emptying paper sheets in a box. The code is available here: https://robotll.github.io/Flipbot/.

A service optimization method for polyester fiber production process is proposed. According to the production batch and production specifications, the method considers the service cost as the optimization objective, and uses data model to determine the specific process parameters in the polyester fiber production process. First, two options for the overall process of polyester fiber are introduced: on-demand manufacturing and product development. Second, the impact of different batch request tasks on the performance index of each stage is determined. Finally, the service optimization measures of different batches are proposed. By comparing the similarity between the current data samples and the overall data, the optimal production plan of the overall production process is formed. Simulation results show that the immune algorithm inspired from endocrine regulation has the best performance on the optimal decision-making combination, which is helpful for the development of new polyester products. We investigate how to reduce energy consumption of system resources, and how to choose the best service from a large number of candidate services. In the overall polyester fiber production process, users are not only consumers, but also designers and producers, achieving the real “integration of production and consumption”.

This paper presents the technique of flex-and-flip manipulation. It is suitable for grasping thin, flexible linear objects lying on a flat surface. During the manipulation process, the object is first flexed by a robotic gripper whose fingers are placed on top of it, and later the increased internal energy of the object helps the gripper obtain a stable pinch grasp while the object flips into the space between the fingers. The dynamic interaction between the flexible object and the gripper is elaborated by analyzing how energy is exchanged. We also discuss the condition on friction to prevent loss of contact. Our flex-and-flip manipulation technique can be implemented with open-loop control and lends itself to underactuated, compliant finger mechanism. A set of experiments in robotic page turning performed with our customized hardware and software system demonstrates the effectiveness and robustness of the manipulation technique.