A 2D floor plan (FP) often contains structural, decorative, and functional elements and annotations. Vectorization of floor plans (VFP) is an object detection task that involves the localization and recognition of different structural primitives in 2D FPs. The detection results can be used to generate 3D models directly. The conventional pipeline of VFP often consists of a series of carefully designed complex algorithms with insufficient generalization ability and suffer from low computing speed. Considering the VFP is not suitable for deep learning-based object detection frameworks, this paper proposed a new VFP framework to solve this problem based on a generative adversarial network (GAN). First, a private dataset called ZSCVFP is established. Unlike current public datasets that only own not more than 5000 black and white samples, ZSCVFP contains 10,800 colorful samples disturbed by decorative textures in different styles. Second, a new edge-extracting GAN (EdgeGAN) is designed for the new task by formulating the VFP task as an image translation task innovatively that involves the projection of the original 2D FPs into a primitive space. The output of EdgeGAN is a primitive feature map, each channel of which only contains one category of the detected primitives in the form of lines. A self-supervising term is introduced to the generative loss of EdgeGAN to ensure the quality of generated images. EdgeGAN is faster than the conventional and object-detection-framework-based pipeline with minimal performance loss. Lastly, two inspection modules that are also suitable for conventional pipelines are proposed to check the connectivity and consistency of PFM based on the subspace connective graph (SCG). The first module contains four criteria that correspond to the sufficient conditions of a fully connected graph. The second module that classifies the category of all subspaces via one single graph neural network (GNN) should be consistent with the text annotations in the original FP (if available). The reason is that GNN treats the adjacent matrix of SCG as weights directly. Thus, GNN can utilize the global layout information and achieve higher accuracy than other common classifying methods. Experimental results are given to illustrate the efficiency of the proposed EdgeGAN and inspection approaches.

To realize ideal force/torque source in applications such as monopod hopping robots and assistive robots, force/torque controlled series elastic actuators(SEA) are widely used components. Most of existing SEA force/torque control approaches are based on the simple SEA whose force/torque output is proportional to the position of the motor with respect to the payload, however the structure of SEAs is becoming more and more complex, which leads to a nonlinear trend in SEA models. To overcome the problem, we propose a general control method, which is not restricted by the SEA mechanical structure, to control SEA output torque based on nonlinear disturbance observer(NDOB). Specifically, the presented strategy yields the first control solution, with guaranteed theoretical analysis, to successfully address the nonlinear SEA torque control problem, with simultaneous payload angular velocity identification as an additional benefit. The tracking performance of the designed control system is theoretically ensured by Lyapunov analysis. Furthermore, taking a nonlinear SEA for instance, simulation and experimental results suggest the effectiveness and superior performance of the proposed method by comparing it with existing method.

Dynamic motion primitive has been the most prevalent model-based imitation learning method in the last few decades. Gaussian mixed regression dynamic motion primitive, which draws upon the strengths of both the motion model and the probability model to cope with multiple demonstrations, is a very practical and conspicuous branch in the dynamic motion primitive family. As Gaussian mixed regression dynamic motion primitive only learns from expert demonstrations and requires full environmental information, it is incapable of handling tasks with unmodeled obstacles. Aiming at this problem, we proposed the positive and negative demonstrations-based dynamic motion primitive, for which the introduction of negative demonstrations can bring additional flexibility. Positive and negative demonstrations-based dynamic motion primitive extends Gaussian mixed regression dynamic motion primitive in three aspects. The first aspect is a new maximum log-likelihood function that balances the probabilities on positive and negative demonstrations. The second one is the positive and negative demonstrations-based expectation–maximum, which involves iteratively calculating the lower bound of a new Q-function. And the last is the application framework of data set aggregation for positive and negative demonstrations-based dynamic motion primitive to handle unmodeled obstacles. Experiments on several typical robot manipulating tasks, which include letter writing, obstacle avoidance, and grasping in a grid box, are conducted to validate the performance of positive and negative demonstrations-based dynamic motion primitive.

Unsupervised domain adaptation (UDA) effectively transfers knowledge learned from a labeled source domain to an unlabeled target domain. The teacher–student framework, which generates pseudo-labels for target domain samples and uses them for pseudo-supervised training, enables self-training and improves generalization in UDA object detection. However, for one-stage detection models, pseudo-labels are unreliable when positive and negative samples are imbalanced. This may lead the model to overfit the source domain and overlook important target-domain information. In this work, we propose a novel domain-specific student–teacher framework to address this issue. The innovations of the proposed framework can be summarized in two aspects. First, we employ two domain-specific heads (DSHs) in the student model to handle inputs from the source domain and the target domain separately. These two heads are optimized independently with samples from their respective domains. This design allows for reducing the impact of unreliable pseudo-labels and fully leveraging unique information specific to the target domain. Second, we introduce an auxiliary reconstruction branch, named the multi-scale mask adversarial alignment (MMAA) module, into the teacher–student framework. The MMAA is tasked with reconstructing randomly masked multi-scale features of the source domain, which enhances the student model’s semantic representation capability and facilitates the generation of high-quality pseudo-labels. Experimental results on six diverse cross-domain scenarios demonstrate the effectiveness of our framework.

Conveyors are used commonly in industrial production lines and automated sorting systems. Many applications require fast, reliable, and dynamic detection and recognition for the objects on conveyors. Aiming at this goal, we design a framework that involves three subtasks: one-class instance segmentation (OCIS), multiobject tracking (MOT), and zero-shot fine-grained recognition of 3D objects (ZSFGR3D). A new level set map network (LSMNet) and a multiview redundancy-free feature network (MVRFFNet) are proposed for the first and third subtasks, respectively. The level set map (LSM) is used to annotate instances instead of the traditional multichannel binary mask, and each peak of the LSM represents one instance. Based on the LSM, LSMNet can adopt a pix2pix architecture to segment instances. MVRFFNet is a generalized zero-shot learning (GZSL) framework based on the Wasserstein generative adversarial network for 3D object recognition. Multi-view features of an object are combined into a compact registered feature. By treating the registered features as the category attribution in the GZSL setting, MVRFFNet learns a mapping function that maps original retrieve features into a new redundancy-free feature space. To validate the performance of the proposed methods, a segmentation dataset and a fine-grained classification dataset about objects on a conveyor are established. Experimental results on these datasets show that LSMNet can achieve a recalling accuracy close to the light instance segmentation framework You Only Look At CoefficienTs (YOLACT), while its computing speed on an NVIDIA GTX1660TI GPU is 80 fps, which is much faster than YOLACT’s 25 fps. Redundancy-free features generated by MVRFFNet perform much better than original features in the retrieval task.