To improve the efficiency of deep reinforcement learning (DRL)-based methods for robotic trajectory planning in the unstructured working environment with obstacles. Different from the traditional sparse reward function, this paper presents two brand-new dense reward functions. First, the azimuth reward function is proposed to accelerate the learning process locally with a more reasonable trajectory by modeling the position and orientation constraints, which can reduce the blindness of exploration dramatically. To further improve the efficiency, a reward function at subtask-level is proposed to provide global guidance for the agent in the DRL. The subtask-level reward function is designed under the assumption that the task can be divided into several subtasks, which reduces the invalid exploration greatly. The extensive experiments show that the proposed reward functions are able to improve the convergence rate by up to three times with the state-of-the-art DRL methods. The percentage increase in convergence means is 2.25%-13.22% and the percentage decreases with respect to standard deviation by 10.8%-74.5%.

Diabetic Retinopathy (DR) is a diabetic complication that predisposes patients to visual impairments that could lead to blindness. Lesion segmentation using deep learning algorithms is an effective measure to screen and prevent early DR. However, there are several types of DR with varying sizes and high inter-class similarity, making segmentation difficult. In this paper, we propose a supervised segmentation method (MSLF-Net) based on multi-scale–multi-level feature fusion to achieve accurate end-to-end DR lesion segmentation. MSLF-Net builds a Multi-Scale Feature Extraction (MSFE) module to extract multi-scale information and provide more comprehensive features for segmentation. This paper further introduces the Multi-Level Feature Fusion (MLFF) module to improve feature fusion using a cross-layer structure. This structure only fuses low- and high-level features of the same class based on category supervision, avoiding feature contamination. Moreover, this paper produces additional masked images for the dataset and performs image enhancement operations to ensure that the proposed method is trainable and functional on small datasets. The extensive experiments are conducted on public datasets IDRID and e_ophtha. The results showed that our proposed feature enhancement method can perform feature fusion more effectively. Therefore, In the end-to-end DR segmentation neural network model, MSLF Net is superior to other similar models in segmentation, and can effectively improve the DR lesion segmentation performance.

To improve the efficiency of surgical trajectory segmentation for surgical assessment and robot learning in robot-assisted minimally invasive surgery, this paper presents a fast unsupervised method using video and kinematic data, followed by a promoting procedure to address the over-segmentation issue. An unsupervised deep learning network called dense convolutional encoder–decoder network (DCED-Net) is first proposed to extract more discriminative features from videos in an effective way. DCED-Net has several advantages. It compresses the encoding–decoding structure, strengthens the feature propagation, and avoids the manual annotation. To further improve the accuracy of segmentation, on one hand, a modified transition state clustering model is employed with a strategy of reducing the redundancy of transition points. On the other hand, the segmentation results are promoted by identifying the over-segmented trajectories based on predefined similarity measurements. Extensive experiments on the public data set JIGSAWS show that with our method, the percentage increase in accuracy is 20.3% and the percentage decrease in time cost is 92.6%.

Dual-arm coordination is a fundamental problem in humanoid nursing robot. Large language model (LLM)-driven dual-arm collaboration is gradually becoming a research hotspot in this field. However, the single-thread LLM task planner lacks the ability of co-scheduling, which leads to poor efficiency in nursing robot. To cope with the problem, this letter proposed a multi-agent LLM solution for the task planning of nursing robot, named DABICO. The framework constructs dual agent systems (left-arm and right-arm) at the levels of communication and decision-making, as well as ensuring a single robot entity. Moreover, we construct corresponding communication mechanism and dialogue protocol to promote the information exchange between the two agents. Finally, validation feedback system is proposed to ensure that the sub-task of each robot arm can be executed successfully. A large set of experiments show that, compared to the single-thread LLM task planner, the DABICO framework is more advantageous when dealing with the bimanual coordination tasks. DABICO is able of accomplishes reasoning rapidly, reducing Replan metrics by $\mathbf{90\%}$ on average, and the improvement with respect to Success rate is $\mathbf{11\%}$ on average. Finally we demonstrate DABICO in real-world medicine organization experiment on a dual-arm nursing robot.

Deep reinforcement learning (DRL) provides a new solution for rehabilitation robot trajectory planning in the unstructured working environment, which can bring great convenience to patients. Previous researches mainly focused on optimization strategies but ignored the construction of reward functions, which leads to low efficiency. Different from traditional sparse reward function, this paper proposes two dense reward functions. First, azimuth reward function mainly provides a global guidance and reasonable constraints in the exploration. To further improve the efficiency, a process-oriented aspiration reward function is proposed, it is capable of accelerating the exploration process and avoid locally optimal solution. Experiments show that the proposed reward functions are able to accelerate the convergence rate by 38.4% on average with the mainstream DRL methods. The mean of convergence also increases by 9.5%, and the percentage of standard deviation decreases by 21.2%–23.3%. Results show that the proposed reward functions can significantly improve learning efficiency of DRL methods, and then provide practical possibility for automatic trajectory planning of rehabilitation robot.

The significance of telerobotic trajectory segmentation has been demonstrated on a range of skill training and robotic automation tasks. However, the trajectories of telerobot are characterized by complexity and high dimensionality, making it difficult to segment them accurately. Existing methods are often plagued by feature inefficiency, and the clustering methods are also difficult to properly model the trajectories. In addition, over-segmentation also affects the accuracy of the clustering-based segmentation methods. To address above problems, this article presents a new unsupervised approach that automatically segments multimodal trajectory data of a telerobot and solves the problem of over-segmentation through a postpromoting procedure. First, we present an unsupervised visual feature-extraction network, that is, a dense connection spatial convolution network (DCSC) to generate more discriminative features for clustering. The dense convolution and spatial convolution facilitates information flow, enhances feature propagation, and avoids manual annotation. Next, we develop an unsupervised trajectory segmentation method that is called multimodality clustering with Chinese restaurant process (MC-CRP). The model utilizes data from different modalities and segments the trajectory through hierarchical clustering. MC-CRP obtains more accurate results in a short period of time. To further improve the precision of trajectory segmentation, we merge over-segments based on predefined similarity measurements. Extensive experiments on the publicly available data set JIGSAWS show that the presented approach achieves 70.1% silhouette coefficient, 25.1% normalized mutual information, and 71.4% segmentation accuracy. These metrics demonstrate that the presented segmentation approach provides deeper insight into the trajectory features and improve the accuracy of segmentation more efficiently than others.