Snap-fit recognition is an essential capability for industrial robots in manufacturing. The goal is to protect fragile parts by quickly detecting snap-fit signals in the assembly. In this letter, we propose a fast recognition method of snap-fit for industrial robots. A snap-fit dataset generation strategy of automatically acquiring labels is presented in the presence of data collection is complicated. A multilayer recurrent neural network (RNN) is designed for snap-fit recognition. An extensive evaluation based on two different datasets shows that the proposed method makes reliable and fast recognitions. Real-time experiments on industrial robot also demonstrate the effectiveness of the proposed method.

The proficiency of robots in cloth manipulation is crucial for their potential widespread deployment in household service contexts, with the task of unfolding cloth being particularly indispensable. Unlike rigid objects, cloth has a high-dimensional state space, which poses significant challenges for robotic operations. This paper presents a robotic framework that integrates dynamic and static operations for cloth unfolding. Dynamic operations are introduced in a single-arm scenario, employing gravity to expedite flattening. Initially, we define the classification of cloth states and operational skills. Subsequently, in skill selection, a Large Language Model (LLM) is utilized to make decisions based on the current state, selecting skills appropriate for the given situation. For the determination of operation points, a cloth region segmentation network extracts key features of the cloth, and the final operation points are determined through geometric analysis of the masks. Experiments on a real robot demonstrate that our method can successfully unfold cloths of various initial conditions, colors, sizes, textures, shapes and materials, achieving over 95% coverage - defined as the ratio of the current area of the fabric to its fully expanded area - thereby proving the effectiveness of the combined dynamic and static operation strategy. Furthermore, this method significantly enhances the efficiency of cloth unfolding, completing the task within ten actions, whereas other methods require dozens of operations, greatly reducing the required operational complexity.

Deformable object manipulation has been considered a challenging task for robots for its complex dynamics and the infinite dimensional configuration space. Fabric unfolding manipulation takes on critical significance in the textile industry and household services. Accordingly, enabling robots to possess the above-mentioned skill has been confirmed as a crucial and challenging task. In this study, a general framework is developed for transferring human skills to robots in fabric unfolding manipulation. The developed framework comprises two key components (i.e., behavior cloning to learn human unfolding policy and learning from demonstration to transfer unfolding actions). A mixture density network is introduced, with the aim of addressing the multimodality in human policy. Moreover, task parameter weighting is considered during action generalization to adapt to a wide variety of unfolding scenarios. As revealed by the experimental results of this study, the framework can successfully unfold fabrics of different colors and sizes, and its performance can be comparable to human-level operation. Furthermore, the framework also can be applied to garment unfolding, and experiments suggest that it exhibits generalization.

A robot screwing skill learning framework based on teaching–learning is proposed to improve the generalization ability of robots for different scenarios and objects, combined with the experience of a human operation. This framework includes task-based teaching, learning, and summarization. We teach a robot to twist and gather the operation’s trajectories, define the obstacles with potential functions, and counter the twisting of the robot using a skill-learning-based dynamic movement primitive (DMP) and Gaussian mixture model–Gaussian mixture regression (GMM-GMR). The hole-finding and screwing stages of the process are modeled. In order to verify the effectiveness of the robot tightening skill learning model and its adaptability to different tightening scenarios, obstacle avoidance trends and tightening experiments were conducted. Obstacle avoidance and tightening experiments were conducted on the robot tightening platform for bolts, plastic bottle caps, and faucets. The robot successfully avoided obstacles and completed the twisting task, verifying the effectiveness of the robot tightening skill learning model and its adaptability to different tightening scenarios.