A promising effective human-robot interaction in assistive robotic systems is gaze-based control. However, current gaze-based assistive systems mainly help users with basic grasping actions, offering limited support. Moreover, the restricted intent recognition capability constrains the assistive system's ability to provide diverse assistance functions. In this paper, we propose an open implicit intention recognition framework powered by Large Language Model (LLM) and Vision Foundation Model (VFM), which can process gaze input and recognize user intents that are not confined to predefined or specific scenarios. Furthermore, we implement a gaze-driven LLM-enhanced assistive robot system (MindEye-OmniAssist) that recognizes user's intentions through gaze and assists in completing task. To achieve this, the system utilizes open vocabulary object detector, intention recognition network and LLM to infer their full intentions. By integrating eye movement feedback and LLM, it generates action sequences to assist the user in completing tasks. Real-world experiments have been conducted for assistive tasks, and the system achieved an overall success rate of 41/55 across various undefined tasks. Preliminary results show that the proposed method holds the potential to provide a more user-friendly human-computer interaction interface and significantly enhance the versatility and effectiveness of assistive systems by supporting more complex and diverse task.

Accurate and robust simultaneous localization and mapping (SLAM) is crucial for autonomous mobile systems, typically achieved by leveraging the geometric features of the environment. Incorporating semantics provides a richer scene representation that not only enhances localization accuracy in SLAM but also enables advanced cognitive functionalities for downstream navigation and planning tasks. Existing point-wise semantic LiDAR SLAM methods often suffer from poor efficiency and generalization, making them less robust in diverse real-world scenarios. In this paper, we propose a semantic graph-enhanced SLAM framework, named SG-SLAM, which effectively leverages the geometric, semantic, and topological characteristics inherent in environmental structures. The semantic graph serves as a fundamental component that facilitates critical functionalities of SLAM, including robust relocalization during odometry failures, accurate loop closing, and semantic graph map construction. Our method employs a dual-threaded architecture, with one thread dedicated to online odometry and relocalization, while the other handles loop closure, pose graph optimization, and map update. This design enables our method to operate in real time and generate globally consistent semantic graph maps and point cloud maps. We extensively evaluate our method across the KITTI, MulRAN, and Apollo datasets, and the results demonstrate its superiority compared to state-of-the-art methods. Our method has been released at https://github.com/nubot-nudt/SG-SLAM.

Effective Human-Robot Interaction (HRI) is crucial for enhancing accessibility and usability in real-world robotics applications. However, existing solutions often rely on gestures or language commands, making interaction inefficient and ambiguous, particularly for users with physical impairments. In this paper, we introduce FAM-HRI, an efficient multi-modal framework for human-robot interaction that integrates language and gaze inputs via foundation models. By leveraging lightweight Meta ARIA glasses, our system captures real-time multi-modal signals and utilizes large language models (LLMs) to fuse user intention with scene context, enabling intuitive and precise robot manipulation. Our method accurately determines gaze fixation time interval, reducing noise caused by the gaze dynamic nature. Experimental evaluations demonstrate that FAM-HRI achieves a high success rate in task execution while maintaining a low interaction time, providing a practical solution for individuals with limited physical mobility or motor impairments.

In the field of robot target recognition research driven by artificial intelligence (AI), factors such as the disordered distribution of targets, the complexity of the environment, the massive scale of data, and noise interference have significantly restricted the improvement of target recognition accuracy. Against the backdrop of the continuous iteration and upgrading of current AI technologies, to meet the demand for accurate recognition of disordered targets by intelligent robots in complex and changeable scenarios, this study innovatively proposes an AI - based intelligent robot disordered target recognition method using reinforcement learning. This method processes the collected target images with the bilateral filtering algorithm, decomposing them into low - illumination images and reflection images. Subsequently, it adopts differentiated AI strategies, compressing the illumination images and enhancing the reflection images respectively, and then fuses the two parts of images to generate a new image. On this basis, this study deeply integrates deep learning, a core AI technology, with the reinforcement learning algorithm. The enhanced target images are input into a deep reinforcement learning model for training, ultimately enabling the AI - based intelligent robot to efficiently recognize disordered targets. Experimental results show that the proposed method can not only significantly improve the quality of target images but also enable the AI - based intelligent robot to complete the recognition task of disordered targets with higher efficiency and accuracy, demonstrating extremely high application value and broad development prospects in the field of AI robots.

To improve the generalization of the autonomous driving (AD) perception model, vehicles need to update the model over time based on the continuously collected data. As time progresses, the amount of data fitted by the AD model expands, which helps to improve the AD model generalization substantially. However, such ever-expanding data is a double-edged sword for the AD model. Specifically, as the fitted data volume grows to exceed the the AD model's fitting capacities, the AD model is prone to under-fitting. To address this issue, we propose to use a pretrained Large Vision Models (LVMs) as backbone coupled with downstream perception head to understand AD semantic information. This design can not only surmount the aforementioned under-fitting problem due to LVMs' powerful fitting capabilities, but also enhance the perception generalization thanks to LVMs' vast and diverse training data. On the other hand, to mitigate vehicles' computational burden of training the perception head while running LVM backbone, we introduce a Posterior Optimization Trajectory (POT)-Guided optimization scheme (POTGui) to accelerate the convergence. Concretely, we propose a POT Generator (POTGen) to generate posterior (future) optimization direction in advance to guide the current optimization iteration, through which the model can generally converge within 10 epochs. Extensive experiments demonstrate that the proposed method improves the performance by over 66.48\% and converges faster over 6 times, compared to the existing state-of-the-art approach.

We propose a real-time dynamic LiDAR odometry pipeline for mobile robots in Urban Search and Rescue (USAR) scenarios. Existing approaches to dynamic object detection often rely on pretrained learned networks or computationally expensive volumetric maps. To enhance efficiency on computationally limited robots, we reuse data between the odometry and detection module. Utilizing a range image segmentation technique and a novel residual-based heuristic, our method distinguishes dynamic from static objects before integrating them into the point cloud map. The approach demonstrates robust object tracking and improved map accuracy in environments with numerous dynamic objects. Even highly non-rigid objects, such as running humans, are accurately detected at point level without prior downsampling of the point cloud and hence, without loss of information. Evaluation on simulated and real-world data validates its computational efficiency. Compared to a state-of-the-art volumetric method, our approach shows comparable detection performance at a fraction of the processing time, adding only 14 ms to the odometry module for dynamic object detection and tracking. The implementation and a new real-world dataset are available as open-source for further research.

In this study, we introduce AV-PedAware, a self-supervised audio-visual fusion system designed to improve dynamic pedestrian awareness for robotics applications. Pedestrian awareness is a critical requirement in many robotics applications. However, traditional approaches that rely on cameras and LIDARs to cover multiple views can be expensive and susceptible to issues such as changes in illumination, occlusion, and weather conditions. Our proposed solution replicates human perception for 3D pedestrian detection using low-cost audio and visual fusion. This study represents the first attempt to employ audio-visual fusion to monitor footstep sounds for the purpose of predicting the movements of pedestrians in the vicinity. The system is trained through self-supervised learning based on LIDAR-generated labels, making it a cost-effective alternative to LIDAR-based pedestrian awareness. AV-PedAware achieves comparable results to LIDAR-based systems at a fraction of the cost. By utilizing an attention mechanism, it can handle dynamic lighting and occlusions, overcoming the limitations of traditional LIDAR and camera-based systems. To evaluate our approach's effectiveness, we collected a new multimodal pedestrian detection dataset and conducted experiments that demonstrate the system's ability to provide reliable 3D detection results using only audio and visual data, even in extreme visual conditions. We will make our collected dataset and source code available online for the community to encourage further development in the field of robotics perception systems.

The lightweight Multi-state Constraint Kalman Filter (MSCKF) has been well-known for its high efficiency, in which the delayed update has been usually adopted since its proposal. This work investigates the immediate update strategy of MSCKF based on timely reconstructed 3D feature points and measurement constraints. The differences between the delayed update and the immediate update are theoretically analyzed in detail. It is found that the immediate update helps construct more observation constraints and employ more filtering updates than the delayed update, which improves the linearization point of the measurement model and therefore enhances the estimation accuracy. Numerical simulations and experiments show that the immediate update strategy significantly enhances MSCKF even with a small amount of feature observations.

This paper presents a framework for dynamic object catching using a quadruped robot's front legs while it stands on its rear legs. The system integrates computer vision, trajectory prediction, and leg control to enable the quadruped to visually detect, track, and successfully catch a thrown object using an onboard camera. Leveraging a fine-tuned YOLOv8 model for object detection and a regression-based trajectory prediction module, the quadruped adapts its front leg positions iteratively to anticipate and intercept the object. The catching maneuver involves identifying the optimal catching position, controlling the front legs with Cartesian PD control, and closing the legs together at the right moment. We propose and validate three different methods for selecting the optimal catching position: 1) intersecting the predicted trajectory with a vertical plane, 2) selecting the point on the predicted trajectory with the minimal distance to the center of the robot's legs in their nominal position, and 3) selecting the point on the predicted trajectory with the highest likelihood on a Gaussian Mixture Model (GMM) modelling the robot's reachable space. Experimental results demonstrate robust catching capabilities across various scenarios, with the GMM method achieving the best performance, leading to an 80% catching success rate. A video demonstration of the system in action can be found at https://youtu.be/sm7RdxRfIYg .

Autonomous vehicles demand detailed maps to maneuver reliably through traffic, which need to be kept up-to-date to ensure a safe operation. A promising way to adapt the maps to the ever-changing road-network is to use crowd-sourced data from a fleet of vehicles. In this work, we present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment including drivable area, lane markings, poles, obstacles and more as a 3D mesh. Each vehicle contributes locally reconstructed submaps as lightweight meshes, making our method applicable to a wide range of reconstruction methods and sensor modalities. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field, which is supervised using the submap meshes to predict a fused environment representation. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction. Our approach is evaluated on two datasets with different local mapping methods, showing improved pose alignment and reconstruction over existing methods. Additionally, we demonstrate the benefit of multi-session mapping and examine the required amount of data to enable high-fidelity map learning for autonomous vehicles.