To enable safe and effective human-robot collaboration (HRC) in smart manufacturing, seamless integration of sensing, cognition, and prediction into the robot controller is critical for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The proposed approach takes advantage of the prediction capabilities of nonlinear model predictive control (NMPC) to execute a safe path planning based on feedback from a vision system. In order to satisfy the requirement of real-time path planning, an embedded solver based on a penalty method is applied. However, due to tight sampling times NMPC solutions are approximate, and hence the safety of the system cannot be guaranteed. To address this we formulate a novel safety-critical paradigm with an exponential control barrier function (ECBF) used as a safety filter. We also design a simple human-robot collaboration scenario using V-REP to evaluate the performance of the proposed controller and investigate whether integrating human pose prediction can help with safe and efficient collaboration. The robot uses OptiTrack cameras for perception and dynamically generates collision-free trajectories to the predicted target interactive position. Results for a number of different configurations confirm the efficiency of the proposed motion planning and execution framework. It yields a 19.8% reduction in execution time for the HRC task considered.
相關內容
Scientific advancements in nanotechnology and advanced materials are paving the way toward nanoscale devices for in-body precision medicine; comprising integrated sensing, computing, communication, data and energy storage capabilities. In the human cardiovascular system, such devices are envisioned to be passively flowing and continuously sensing for detecting events of diagnostic interest. The diagnostic value of detecting such events can be enhanced by assigning to them their physical locations (e.g., body region), which is the main proposition of flow-guided localization. Current flow-guided localization approaches suffer from low localization accuracy and they are by-design unable to localize events within the entire cardiovascular system. Toward addressing this issue, we propose the utilization of Graph Neural Networks (GNNs) for this purpose, and demonstrate localization accuracy and coverage enhancements of our proposal over the existing State of the Art (SotA) approaches. Based on our evaluation, we provide several design guidelines for GNN-enabled flow-guided localization.
Large-scale driving datasets such as Waymo Open Dataset and nuScenes substantially accelerate autonomous driving research, especially for perception tasks such as 3D detection and trajectory forecasting. Since the driving logs in these datasets contain HD maps and detailed object annotations which accurately reflect the real-world complexity of traffic behaviors, we can harvest a massive number of complex traffic scenarios and recreate their digital twins in simulation. Compared to the hand-crafted scenarios often used in existing simulators, data-driven scenarios collected from the real world can facilitate many research opportunities in machine learning and autonomous driving. In this work, we present ScenarioNet, an open-source platform for large-scale traffic scenario modeling and simulation. ScenarioNet defines a unified scenario description format and collects a large-scale repository of real-world traffic scenarios from the heterogeneous data in various driving datasets including Waymo, nuScenes, Lyft L5, and nuPlan datasets. These scenarios can be further replayed and interacted with in multiple views from Bird-Eye-View layout to realistic 3D rendering in MetaDrive simulator. This provides a benchmark for evaluating the safety of autonomous driving stacks in simulation before their real-world deployment. We further demonstrate the strengths of ScenarioNet on large-scale scenario generation, imitation learning, and reinforcement learning in both single-agent and multi-agent settings. Code, demo videos, and website are available at //metadriverse.github.io/scenarionet.
System-level testing of healthcare Internet of Things (IoT_ applications requires creating a test infrastructure with integrated medical devices and third-party applications. A significant challenge in creating such test infrastructure is that healthcare IoT applications evolve continuously with the addition of new medical devices from different vendors and new services offered by different third-party organizations following different architectures. Moreover, creating test infrastructure with a large number of different types of medical devices is time-consuming, financially expensive, and practically infeasible. Oslo City healthcare department faced these challenges while working with various healthcare IoT applications. This paper presents a real-world software architecture (HITA) to create a test infrastructure for healthcare IoT applications. We discuss the quality requirements achieved by HITA and the status of work products developing as a part of HITA. We also present our experiences and lessons learned from the architectural work related to HITA.
Co-design involves simultaneously optimizing the controller and agents physical design. Its inherent bi-level optimization formulation necessitates an outer loop design optimization driven by an inner loop control optimization. This can be challenging when the design space is large and each design evaluation involves data-intensive reinforcement learning process for control optimization. To improve the sample-efficiency we propose a multi-fidelity-based design exploration strategy based on Hyperband where we tie the controllers learnt across the design spaces through a universal policy learner for warm-starting the subsequent controller learning problems. Further, we recommend a particular way of traversing the Hyperband generated design matrix that ensures that the stochasticity of the Hyperband is reduced the most with the increasing warm starting effect of the universal policy learner as it is strengthened with each new design evaluation. Experiments performed on a wide range of agent design problems demonstrate the superiority of our method compared to the baselines. Additionally, analysis of the optimized designs shows interesting design alterations including design simplifications and non-intuitive alterations that have emerged in the biological world.
Search-based software testing (SBST) typically relies on fitness functions to guide the search exploration toward software failures. There are two main techniques to define fitness functions: (a) automated fitness function computation from the specification of the system requirements, and (b) manual fitness function design. Both techniques have advantages. The former uses information from the system requirements to guide the search toward portions of the input domain more likely to contain failures. The latter uses the engineers' domain knowledge. We propose ATheNA, a novel SBST framework that combines fitness functions automatically generated from requirements specifications and those manually defined by engineers. We design and implement ATheNA-S, an instance of ATheNA that targets Simulink models. We evaluate ATheNA-S by considering a large set of models from different domains. Our results show that ATheNA-S generates more failure-revealing test cases than existing baseline tools and that the difference between the runtime performance of ATheNA-S and the baseline tools is not statistically significant. We also assess whether ATheNA-S could generate failure-revealing test cases when applied to two representative case studies: one from the automotive domain and one from the medical domain. Our results show that ATheNA-S successfully revealed a requirement violation in our case studies.
Generative models (GMs) have received increasing research interest for their remarkable capacity to achieve comprehensive understanding. However, their potential application in the domain of multi-modal tracking has remained relatively unexplored. In this context, we seek to uncover the potential of harnessing generative techniques to address the critical challenge, information fusion, in multi-modal tracking. In this paper, we delve into two prominent GM techniques, namely, Conditional Generative Adversarial Networks (CGANs) and Diffusion Models (DMs). Different from the standard fusion process where the features from each modality are directly fed into the fusion block, we condition these multi-modal features with random noise in the GM framework, effectively transforming the original training samples into harder instances. This design excels at extracting discriminative clues from the features, enhancing the ultimate tracking performance. To quantitatively gauge the effectiveness of our approach, we conduct extensive experiments across two multi-modal tracking tasks, three baseline methods, and three challenging benchmarks. The experimental results demonstrate that the proposed generative-based fusion mechanism achieves state-of-the-art performance, setting new records on LasHeR and RGBD1K.
To protect the intellectual property of well-trained deep neural networks (DNNs), black-box DNN watermarks, which are embedded into the prediction behavior of DNN models on a set of specially-crafted samples, have gained increasing popularity in both academy and industry. Watermark robustness is usually implemented against attackers who steal the protected model and obfuscate its parameters for watermark removal. Recent studies empirically prove the robustness of most black-box watermarking schemes against known removal attempts. In this paper, we propose a novel Model Inversion-based Removal Attack (\textsc{Mira}), which is watermark-agnostic and effective against most of mainstream black-box DNN watermarking schemes. In general, our attack pipeline exploits the internals of the protected model to recover and unlearn the watermark message. We further design target class detection and recovered sample splitting algorithms to reduce the utility loss caused by \textsc{Mira} and achieve data-free watermark removal on half of the watermarking schemes. We conduct comprehensive evaluation of \textsc{Mira} against ten mainstream black-box watermarks on three benchmark datasets and DNN architectures. Compared with six baseline removal attacks, \textsc{Mira} achieves strong watermark removal effects on the covered watermarks, preserving at least $90\%$ of the stolen model utility, under more relaxed or even no assumptions on the dataset availability.
Owing to effective and flexible data acquisition, unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). Inspired by recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks, such as environmental monitoring, precision agriculture, traffic management. This paper provides a comprehensive survey on the research progress and prospects of DL-based UAV object detection and tracking methods. More specifically, we first outline the challenges, statistics of existing methods, and provide solutions from the perspectives of DL-based models in three research topics: object detection from the image, object detection from the video, and object tracking from the video. Open datasets related to UAV-dominated object detection and tracking are exhausted, and four benchmark datasets are employed for performance evaluation using some state-of-the-art methods. Finally, prospects and considerations for the future work are discussed and summarized. It is expected that this survey can facilitate those researchers who come from remote sensing field with an overview of DL-based UAV object detection and tracking methods, along with some thoughts on their further developments.
Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications. However, static recommendation models are difficult to answer two important questions well due to inherent shortcomings: (a) What exactly does a user like? (b) Why does a user like an item? The shortcomings are due to the way that static models learn user preference, i.e., without explicit instructions and active feedback from users. The recent rise of conversational recommender systems (CRSs) changes this situation fundamentally. In a CRS, users and the system can dynamically communicate through natural language interactions, which provide unprecedented opportunities to explicitly obtain the exact preference of users. Considerable efforts, spread across disparate settings and applications, have been put into developing CRSs. Existing models, technologies, and evaluation methods for CRSs are far from mature. In this paper, we provide a systematic review of the techniques used in current CRSs. We summarize the key challenges of developing CRSs into five directions: (1) Question-based user preference elicitation. (2) Multi-turn conversational recommendation strategies. (3) Dialogue understanding and generation. (4) Exploitation-exploration trade-offs. (5) Evaluation and user simulation. These research directions involve multiple research fields like information retrieval (IR), natural language processing (NLP), and human-computer interaction (HCI). Based on these research directions, we discuss some future challenges and opportunities. We provide a road map for researchers from multiple communities to get started in this area. We hope this survey helps to identify and address challenges in CRSs and inspire future research.
The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction). Several recent works suggest that convolutional neural network (CNN) based models generate richer and more expressive feature embeddings and hence also perform well on relation prediction. However, we observe that these KG embeddings treat triples independently and thus fail to cover the complex and hidden information that is inherently implicit in the local neighborhood surrounding a triple. To this effect, our paper proposes a novel attention based feature embedding that captures both entity and relation features in any given entity's neighborhood. Additionally, we also encapsulate relation clusters and multihop relations in our model. Our empirical study offers insights into the efficacy of our attention based model and we show marked performance gains in comparison to state of the art methods on all datasets.
小貼士
登錄享
相關主題
注冊地址： 北京市海淀區羊坊店路18號2幢3層301-191