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For a long time, in reconstructing 3D human bodies from monocular images, most methods opted to simplify the task by minimizing the influence of the camera. Using a coarse focal length setting results in the reconstructed bodies not aligning well with distorted images. Ignoring camera rotation leads to an unrealistic reconstructed body pose in world space. Consequently, the application scenarios of existing methods are confined to controlled environments. When confronted with complex and diverse in-the-wild images, they struggle to achieve accurate and reasonable reconstruction in world space. To address the above issues, we propose W-HMR, which decouples global body recovery into camera calibration, local body recovery, and global body orientation correction. We design the first weak-supervised camera calibration method for body distortion, eliminating dependence on focal length labels and achieving finer mesh-image alignment. We propose a novel orientation correction module to allow the reconstructed human body to remain normal in world space. Decoupling body orientation and body pose enables our model to consider the accuracy in camera coordinate and the reasonableness in world coordinate simultaneously, expanding the range of applications. As a result, W-HMR achieves high-quality reconstruction in dual coordinate systems, particularly in challenging scenes. Codes and demos have been released on the project page //yw0208.github.io/w-hmr/.

Despite the recent progress on 6D object pose estimation methods for robotic grasping, a substantial performance gap persists between the capabilities of these methods on existing datasets and their efficacy in real-world mobile manipulation tasks, particularly when robots rely solely on their monocular egocentric field of view (FOV). Existing real-world datasets primarily focus on table-top grasping scenarios, where a robotic arm is placed in a fixed position and the objects are centralized within the FOV of fixed external camera(s). Assessing performance on such datasets may not accurately reflect the challenges encountered in everyday mobile manipulation tasks within kitchen environments such as retrieving objects from higher shelves, sinks, dishwashers, ovens, refrigerators, or microwaves. To address this gap, we present Kitchen, a novel benchmark designed specifically for estimating the 6D poses of objects located in diverse positions within kitchen settings. For this purpose, we recorded a comprehensive dataset comprising around 205k real-world RGBD images for 111 kitchen objects captured in two distinct kitchens, utilizing one humanoid robot with its egocentric perspectives. Subsequently, we developed a semi-automated annotation pipeline, to streamline the labeling process of such datasets, resulting in the generation of 2D object labels, 2D object segmentation masks, and 6D object poses with minimized human effort. The benchmark, the dataset, and the annotation pipeline are available at //kitchen-dataset.github.io/KITchen.

Large-scale swarm robotic systems consisting of numerous cooperative agents show considerable promise for performing autonomous tasks across various sectors. Nonetheless, traditional motion planning approaches often face a trade-off between scalability and solution quality due to the exponential growth of the joint state space of robots. In response, this work proposes SwarmPRM, a hierarchical, scalable, computationally efficient, and risk-aware sampling-based motion planning approach for large-scale swarm robots. SwarmPRM utilizes a Gaussian Mixture Model (GMM) to represent the swarm's macroscopic state and constructs a Probabilistic Roadmap in Gaussian space, referred to as the Gaussian roadmap, to generate a transport trajectory of GMM. This trajectory is then followed by each robot at the microscopic stage. To enhance trajectory safety, SwarmPRM incorporates the conditional value-at-risk (CVaR) in the collision checking process to impart the property of risk awareness to the constructed Gaussian roadmap. SwarmPRM then crafts a linear programming formulation to compute the optimal GMM transport trajectory within this roadmap. Extensive simulations demonstrate that SwarmPRM outperforms state-of-the-art methods in computational efficiency, scalability, and trajectory quality while offering the capability to adjust the risk tolerance of generated trajectories.

We develop a hierarchical LLM-task-motion planning and replanning framework to efficiently ground an abstracted human command into tangible Autonomous Underwater Vehicle (AUV) control through enhanced representations of the world. We also incorporate a holistic replanner to provide real-world feedback with all planners for robust AUV operation. While there has been extensive research in bridging the gap between LLMs and robotic missions, they are unable to guarantee success of AUV applications in the vast and unknown ocean environment. To tackle specific challenges in marine robotics, we design a hierarchical planner to compose executable motion plans, which achieves planning efficiency and solution quality by decomposing long-horizon missions into sub-tasks. At the same time, real-time data stream is obtained by a replanner to address environmental uncertainties during plan execution. Experiments validate that our proposed framework delivers successful AUV performance of long-duration missions through natural language piloting.

This paper presents an innovative large language model (LLM)-based robotic system for enhancing multi-modal human-robot interaction (HRI). Traditional HRI systems relied on complex designs for intent estimation, reasoning, and behavior generation, which were resource-intensive. In contrast, our system empowers researchers and practitioners to regulate robot behavior through three key aspects: providing high-level linguistic guidance, creating "atomic actions" and expressions the robot can use, and offering a set of examples. Implemented on a physical robot, it demonstrates proficiency in adapting to multi-modal inputs and determining the appropriate manner of action to assist humans with its arms, following researchers' defined guidelines. Simultaneously, it coordinates the robot's lid, neck, and ear movements with speech output to produce dynamic, multi-modal expressions. This showcases the system's potential to revolutionize HRI by shifting from conventional, manual state-and-flow design methods to an intuitive, guidance-based, and example-driven approach. Supplementary material can be found at //hri-eu.github.io/Lami/

Digitising the 3D world into a clean, CAD model-based representation has important applications for augmented reality and robotics. Current state-of-the-art methods are computationally intensive as they individually encode each detected object and optimise CAD alignments in a second stage. In this work, we propose FastCAD, a real-time method that simultaneously retrieves and aligns CAD models for all objects in a given scene. In contrast to previous works, we directly predict alignment parameters and shape embeddings. We achieve high-quality shape retrievals by learning CAD embeddings in a contrastive learning framework and distilling those into FastCAD. Our single-stage method accelerates the inference time by a factor of 50 compared to other methods operating on RGB-D scans while outperforming them on the challenging Scan2CAD alignment benchmark. Further, our approach collaborates seamlessly with online 3D reconstruction techniques. This enables the real-time generation of precise CAD model-based reconstructions from videos at 10 FPS. Doing so, we significantly improve the Scan2CAD alignment accuracy in the video setting from 43.0% to 48.2% and the reconstruction accuracy from 22.9% to 29.6%.

We propose a data-driven control method for systems with aleatoric uncertainty, for example, robot fleets with variations between agents. Our method leverages shared trajectory data to increase the robustness of the designed controller and thus facilitate transfer to new variations without the need for prior parameter and uncertainty estimations. In contrast to existing work on experience transfer for performance, our approach focuses on robustness and uses data collected from multiple realizations to guarantee generalization to unseen ones. Our method is based on scenario optimization combined with recent formulations for direct data-driven control. We derive lower bounds on the amount of data required to achieve quadratic stability for probabilistic systems with aleatoric uncertainty and demonstrate the benefits of our data-driven method through a numerical example. We find that the learned controllers generalize well to high variations in the dynamics even when based on only a few short open-loop trajectories. Robust experience transfer enables the design of safe and robust controllers that work out of the box without any additional learning during deployment.

The advent of Transformers has revolutionized computer vision, offering a powerful alternative to convolutional neural networks (CNNs), especially with the local attention mechanism that excels at capturing local structures within the input and achieve state-of-the-art performance. Processing in-memory (PIM) architecture offers extensive parallelism, low data movement costs, and scalable memory bandwidth, making it a promising solution to accelerate Transformer with memory-intensive operations. However, the crucial challenge lies in efficiently deploying the entire model onto a resource-limited PIM system while parallelizing each transformer block with potentially many computational branches based on local attention mechanisms. We present Allspark, which focuses on workload orchestration for visual Transformers on PIM systems, aiming at minimizing inference latency. Firstly, to fully utilize the massive parallelism of PIM, Allspark empolys a finer-grained partitioning scheme for computational branches, and format a systematic layout and interleaved dataflow with maximized data locality and reduced data movement. Secondly, Allspark formulates the scheduling of the complete model on a resource-limited distributed PIM system as an integer linear programming (ILP) problem. Thirdly, as local-global data interactions exhibit complex yet regular dependencies, Allspark provides a greedy-based mapping method to allocate computational branches onto the PIM system and minimize NoC communication costs. Extensive experiments on 3D-stacked DRAM-based PIM systems show that Allspark brings 1.2x-24.0x inference speedup for various visual Transformers over baselines, and that Allspark-enriched PIM system yields average speedups of 2.3x and energy savings of 20x-55x over Nvidia V100 GPU.

Understanding the modus operandi of adversaries aids organizations in employing efficient defensive strategies and sharing intelligence in the community. This knowledge is often present in unstructured natural language text within threat analysis reports. A translation tool is needed to interpret the modus operandi explained in the sentences of the threat report and translate it into a structured format. This research introduces a methodology named TTPXHunter for the automated extraction of threat intelligence in terms of Tactics, Techniques, and Procedures (TTPs) from finished cyber threat reports. It leverages cyber domain-specific state-of-the-art natural language processing (NLP) to augment sentences for minority class TTPs and refine pinpointing the TTPs in threat analysis reports significantly. The knowledge of threat intelligence in terms of TTPs is essential for comprehensively understanding cyber threats and enhancing detection and mitigation strategies. We create two datasets: an augmented sentence-TTP dataset of 39,296 samples and a 149 real-world cyber threat intelligence report-to-TTP dataset. Further, we evaluate TTPXHunter on the augmented sentence dataset and the cyber threat reports. The TTPXHunter achieves the highest performance of 92.42% f1-score on the augmented dataset, and it also outperforms existing state-of-the-art solutions in TTP extraction by achieving an f1-score of 97.09% when evaluated over the report dataset. TTPXHunter significantly improves cybersecurity threat intelligence by offering quick, actionable insights into attacker behaviors. This advancement automates threat intelligence analysis, providing a crucial tool for cybersecurity professionals fighting cyber threats.

Robotic collectives for military and disaster response applications require coalition formation algorithms to partition robots into appropriate task teams. Collectives' missions will often incorporate tasks that require multiple high-level robot behaviors or services, which coalition formation must accommodate. The highly dynamic and unstructured application domains also necessitate that coalition formation algorithms produce near optimal solutions (i.e., >95% utility) in near real-time (i.e., <5 minutes) with very large collectives (i.e., hundreds of robots). No previous coalition formation algorithm satisfies these requirements. An initial evaluation found that traditional auction-based algorithms' runtimes are too long, even though the centralized simulator incorporated ideal conditions unlikely to occur in real-world deployments (i.e., synchronization across robots and perfect, instantaneous communication). The hedonic game-based GRAPE algorithm can produce solutions in near real-time, but cannot be applied to multiple service collectives. This manuscript integrates GRAPE and a services model, producing GRAPE-S and Pair-GRAPE-S. These algorithms and two auction baselines were evaluated using a centralized simulator with up to 1000 robots, and via the largest distributed coalition formation simulated evaluation to date, with up to 500 robots. The evaluations demonstrate that auctions transfer poorly to distributed collectives, resulting in excessive runtimes and low utility solutions. GRAPE-S satisfies the target domains' coalition formation requirements, producing near optimal solutions in near real-time, and Pair-GRAPE-S more than satisfies the domain requirements, producing optimal solutions in near real-time. GRAPE-S and Pair-GRAPE-S are the first algorithms demonstrated to support near real-time coalition formation for very large, distributed collectives with multiple services.