本期机器人顶刊精选

Accurate measures of muscle forces are critical for engineers, clinicians, and biomechanists, yet direct measurement is highly invasive, and current estimation methods remain limited in accuracy. Here, we demonstrate the application of ultra-wideband radar sensors to noninvasively estimate muscle forces by measuring changes in the electromagnetic properties of contracting muscles, in muscles with different structural properties, during various static and dynamic conditions, and in the presence of fatigue. First, we show that ultra-wideband radar scans of muscle can reliably track isometric force in a unipennate knee extensor (vastus lateralis) and a bipennate ankle dorsiflexor (tibialis anterior). Next, we integrate radar signals in machine learning and linear models to estimate muscle forces during fatiguing isometric and dynamic knee extension contractions, with exceptional accuracy. Last, we identify radar frequency–dependent effects of the relationship between muscle forces and ultra-wideband radar signals, with these effects being independent of physiological and structural features known to influence muscle force. Together, these findings establish ultra-wideband radar as a noninvasive sensor capable of quantifying in vivo muscle forces, with the potential for wearable assistive technologies, biomechanics, and rehabilitation.

Real-world robotic manipulation in homes and factories demands reliability, efficiency, and robustness that approach or surpass skilled human operators. We present a real-world reinforcement learning (RL) framework, RL-100, for achieving complete task success under a predefined evaluation protocol built on diffusion visuomotor policies. RL-100 unifies imitation and RL under a single clipped proximal policy optimization surrogate objective applied in the denoising process, yielding conservative, stable improvements across offline and online stages. To meet deployment latency, a lightweight consistency distillation compresses multistep diffusion into a one-step controller for high-frequency control. The framework is task, embodiment, and representation agnostic and supports both single-action and action-chunking control. We evaluated RL-100 on eight diverse real-robot tasks, from pushing and bowling to pouring, cloth folding, unscrewing, multistage juicing, and long-horizon box folding. Under our predefined protocol, RL-100 achieved 100% success in the evaluated trials (1000 of 1000 episodes), including up to 250 of 250 consecutive trials on one task. It matched or surpassed expert teleoperators in time to completion. Without retraining, a single policy attained ∼90% zero-shot success under environmental and dynamics shifts, adapted in a few-shot regime to substantial task variations (86.7%), and remained robust to human perturbations (about 96%). Our juicing robot served customers continuously for about 7 hours without failure when deployed zero-shot in a shopping mall. These results suggest a potential path to deployable robot learning by starting from human priors, aligning training objectives with human-grounded metrics, and reliably extending performance beyond human demonstrations.

As robots enter homes, workplaces, and health care settings, sustaining trust during social interaction becomes a central challenge for human-robot interaction. However, relatively little is known about how humans integrate signals across the brain, hormones, mind, and behavior when robots violate expectations or display social expressiveness. Addressing this gap, we examined how robot performance (congruent versus erroneous) and expressiveness (animated versus stationary) shape multilevel human responses during face-to-face decision-making with an embodied humanoid robot. Participants engaged with the robot in person while neural activity was monitored using functional near-infrared spectroscopy, alongside salivary oxytocin assays, self-reported trust, and behavioral influence measures. Robot errors, implemented as cooperative norm violations, reliably reduced trust and influence, establishing performance reliability as the foundation of trust. Expressiveness amplified these effects: Animated robots elicited stronger prefrontal engagement and cross-level neural-hormonal coupling. Elevated oxytocin was most strongly linked to reduced trust during expressive robot errors, alongside diminished behavioral influence. This pattern is consistent with a context-sensitive vigilance response, indicating that oxytocin in human-robot interaction may heighten sensitivity to norm violations rather than reliably promote bonding. Validation analyses provided small, directionally consistent support for this pattern under counterbalanced order and improved temporal separation. Together, these findings establish a multilevel framework for studying trust in human-robot interaction and reveal a critical design trade-off: Expressive design enhances engagement but can make robot errors disproportionately damaging to trust. These insights identify a biologically grounded boundary condition for oxytocin’s role in social interaction and inform the design of socially effective and trustworthy robots.

Seamless operation of mobile robots in challenging environments requires low-latency local motion estimation and accurate global localization. While most sensor-fusion approaches are designed for specific scenarios, this work introduces a flexible open-source solution for task- and setup-agnostic multimodal sensor fusion distinguished by its generality and usability. Holistic Fusion formulates sensor fusion as a combined estimation problem of i) the local and global robot state and ii) a (theoretically unlimited) number of dynamic variables, including automatic alignment of reference frames; this formulation fits countless real-world applications without conceptual modifications, offering a comprehensive solution beyond hard-coded/task-specific approaches. The proposed factor-graph formulation enables direct fusion of an arbitrary number of absolute, local, and landmark measurements expressed with respect to different frames by explicitly including them as states in the optimization and modeling their evolution as random walks. Moreover, local smoothness and consistency receive particular attention to prevent estimation jumps. Holistic Fusion enables low-latency and smooth online state estimation on typical robot hardware while simultaneously providing low-drift global localization at the IMU measurement rate. The efficacy of this released framework [1] is demonstrated in five real-world scenarios on three robotic platforms with distinct task requirements, highlighting the advantages of fusing multiple absolute measurement types [2]. [1] Code: https://github.com/leggedrobotics/holistic_fusion [2] Project: https://leggedrobotics.github.io/holistic_fusion

Humanoid and quadrupedal robots have the potential to revolutionize the way we work, interact, and coexist with intelligent machines. To understand their effects on society and how they can enable scientific discovery, we assess the current capabilities of these systems along hardware, locomotion, autonomy, data, and applications. We identify recent advances and key open challenges that must be overcome to enable widespread adoption and new use cases for legged robots. Last, we provide an outlook on the future of legged robots, exploring their ethical considerations, economic potential, policy implications, and broader societal effects.

Swarm navigation of micro-/nanorobots has been attracting extensive attention, as it is a vital technique for microrobotic applications, for example, targeted drug delivery/therapy and micromanipulation. Researchers have shown that, controlled by a global magnetic field, millions of micro-/nanorobots can assemble and then efficiently navigate to targeted locations. However, current navigation control schemes for microswarms do not consider constraints on swarm actuation, for example, motion direction and rotating angular velocity, which would result in non-optimal navigation performance and even failure. In this work, we propose an actuation-constrained control framework for microswarms that explicitly treats important swarm motion properties as hard constraints in both trajectory planning and motion control. In our framework, we derive a constrained coarse-to-fine differential dynamic programming (DDP)-based trajectory planner that can generate the optimal trajectory in obstacle environments under the constraint of microswarm rotational angular velocity to maintain stable swarm assembly. Regarding trajectory tracking, we formulate a nonholonomic model predictive control (MPC) scheme, which makes the swarm optimally track the planned trajectory while complying with constraints on motion direction and rotational angular velocity. By our framework, microswarms can navigate with stable swarm assembly and the fastest motion speed, realizing optimal navigation performances. A series of comparative simulations and experiments validate the advantages of our framework in terms of swarm stability and navigation speed. Experimental results also show that our framework can work with different environmental morphologies, for example, discrete obstacles and channels, showing high adaptability to working scenarios. Navigation with moving obstacles and fluid flow further proves the capability of our framework for dynamic environments.

The three-dimensional divergent component of motion (3D-DCM) framework has been successfully utilized to generate center of mass (CoM) trajectories for various locomotion types. While the 3D-DCM encodes the CoM dynamics, it relies on the contact forces between the robot’s end effectors and the environment to realize the intended motion. In the original formulation of the 3D-DCM, the feasibility of contact forces concerning contact constraints is assumed, but a comprehensive analysis of this assumption is lacking. In this work, we address this gap by extending the 3D-DCM framework to incorporate contact constraints and dynamic stability of the system. This is achieved by encoding feasible CoM forces as geometric sets. We derive an analytical relationship that characterizes how these sets can be modulated by humanoid push recovery strategies. Building on these insights, we propose a push recovery algorithm that integrates ankle, hip, height-variation, and stepping strategies. The proposed method is evaluated through extensive experiments with the humanoid robot TORO, including scenarios of force-disturbed balancing, walking, and multi-contact configurations.

Non-convex constrained optimizations are ubiquitous in robotic applications such as multi-agent navigation, UAV trajectory optimization, and soft robot simulation. As a common feature in these problems, the associated non-convex constraints, including collision constraints, inversion-free constraints, and strain limits, are also non-smooth with ill-defined gradients. It is well-known that such constraints are notoriously difficult to handle, for which off-the-shelf optimizers can fail catastrophically. Instead, prior works tend to design problem-specific optimizers that trade performance for robustness. To efficiently solve this problem class in a unified manner, we propose a variant of alternating direction method of multiplier (ADMM), called BC-ADMM. Over the past decade, ADMM has achieved great success in efficiently solving many large-scale (constrained) optimization problems by decoupling them into subproblems that can be solved in parallel. However, prior ADMM algorithms lack a convergence guarantee when handling a large number of non-convex constraints with loopy constraint graphs. Instead, our BC-ADMM relaxes each non-convex constraint into a bi-convex function, further breaking the constraint into two subproblems. We show that such relaxation leads to a variant of ADMM with convergence speed guarantees under appropriate parameter choices. We further provide a practical algorithm under much milder assumptions on the parameter choices, with convergence guarantees without a speed bound. Through numerical experiments in a row of four robotic applications, we show that BC-ADMM has faster convergence than conventional gradient descent and Newton’s method in terms of wall clock time.

We present an online model-based reinforcement learning algorithm suitable for controlling complex robotic systems directly in the real world. Unlike prevailing sim-to-real pipelines that rely on extensive offline simulation and model-free policy optimization, our method builds a dynamics model from real-time interaction data and performs policy updates guided by the learned dynamics model. This efficient model-based reinforcement learning scheme significantly reduces the number of samples to train control policies, enabling direct training on real-world rollout data. This significantly reduces the influence of bias in the simulated data, and facilitates the search for high-performance control policies. We adopt online optimization analysis to derive sublinear regret bounds under stochastic online optimization assumptions, providing formal guarantees on performance improvement as more interaction data are collected. Experimental evaluations were performed on a hydraulic excavator arm and a soft robot arm, where the algorithm demonstrates strong sample efficiency compared to model-free reinforcement learning methods, reaching comparable performance within hours. Robust adaptation to shifting dynamics was also observed when the payload condition was randomized. Our approach paves the way toward efficient and reliable on-robot learning for a broad class of challenging control tasks.

Legged robots must achieve both robust locomotion and energy efficiency to be practical in real-world environments. Yet controllers trained in simulation often fail to transfer reliably, and most existing approaches neglect actuator-specific energy losses or depend on complex, hand-tuned reward formulations. We propose a framework that integrates sim-to-real reinforcement learning with a physics-grounded energy model for permanent magnet synchronous motors. The framework requires a minimal parameter set to capture the simulation–reality gap and employs a compact four-term reward with a first-principle-based energetic loss formulation that balances electrical and mechanical dissipation. We evaluate and validate the approach through a bottom-up dynamic parameter identification study, spanning actuators, full-robot in-air trajectories and on-ground locomotion. The framework is tested on three primary platforms and deployed on 10 additional robots, demonstrating reliable policy transfer without randomization of dynamic parameters. Our method improves the energetic efficiency over state-of-the-art methods, achieving a 32% reduction in the full Cost of Transport of anymal (1.27). All code, models, and datasets are publicly available.

Ultra-wideband radar identifies components of a muscle’s contractile state to estimate muscle force.

The distinction is a matter of life and death to both a predator and a robot in the 2025 movie Predator: Badlands .

Language-conditioned robot manipulation is an emerging field aimed at enabling seamless communication and cooperation between humans and robotic agents by teaching robots to comprehend and execute instructions conveyed in natural language. This interdisciplinary area integrates scene understanding, language processing, and policy learning to bridge the gap between human instructions and robot actions. In this comprehensive survey, we systematically explore recent advancements in language-conditioned robot manipulation. We categorize existing methods based on the primary ways language is integrated into the robot system, namely language for state evaluation, language as a policy condition, language for cognitive planning and reasoning, and language in unified vision-language-action models. Specifically, we further analyze state-of-the-art techniques from five axes of action granularity, data and supervision regimes, system cost and latency, environments and evaluations, and task specification. Additionally, we highlight the key debates in the field. Finally, we discuss open challenges and future research directions, focusing on potentially enhancing generalization capabilities and addressing safety issues in language-conditioned robot manipulators.

Learning-based methods excel at robot motion generation but remain limited in contact-rich physical interaction. Impedance control provides stable and safe contact behavior but requires task-specific tuning of stiffness and damping parameters. We present Diffusion-Based Impedance Learning, a framework that bridges these paradigms by combining generative modeling with energy-consistent impedance control. A Transformer-based Diffusion Model, conditioned via cross-attention on measured external wrenches, reconstructs simulated Zero-Force Trajectories (sZFTs) that represent contact-consistent equilibrium behavior. A SLERP-based quaternion noise scheduler preserves geometric consistency for rotations on the unit sphere. The reconstructed sZFT is used by an energy-based estimator to adapt impedance online through directional stiffness and damping modulation. Trained on parkour and robot-assisted therapy demonstrations collected via Apple Vision Pro teleoperation, the model achieves sub-millimeter positional and sub-degree rotational accuracy using only tens of thousands of samples. Deployed in realtime torque control on a KUKA LBR iiwa, the approach enables smooth obstacle traversal and generalizes to unseen tasks, achieving 100% success in multi-geometry peg-in-hole insertion.

To survive in dynamic and unstructured environments, animals have developed extraordinary multimodal locomotion capabilities. This biological insight drives the innovation of bioinspired multimodal robots, which are defined as robotic systems integrating and transitioning between two or more distinct modes of bioinspired locomotion. This Review explores the historical progression, key design considerations, and current challenges faced in the realm of multimodal robotics. We highlight recent advancements in robotic body design, including the integration of soft materials, innovative structure repurposing strategies, and the deployment of multirobot systems. These advancements facilitate seamless mode transitions through both active and passive structural reconfigurations. In the realm of path planning and motion control, the paradigm is progressively shifting from traditional graph-based approaches and discrete controllers to learning-based frameworks. To address the current void of standardized benchmarks in this field, we propose five performance metrics—number of modes, marginal cost of modality, component repurpose percentage, transition cost, and performance improvement—that provide a quantitative framework for evaluating multimodal robots in terms of design effectiveness and operational performance. Last, we delineate a strategic roadmap for the future that advocates for the integration of physical and computational intelligence in multimodal robotics, arguing that this convergence is crucial for enabling real-time behavioral adaptations to complex environmental stimuli and thereby enhancing the robustness and functionality of multimodal robotic systems.

Degeneracy caused by the repetition of geometric features remains a significant bottleneck in robotic state estimation systems like odometry and SLAM. To address this challenge, we propose a novel approach based on screw theory to model feature constraints and analyze degeneracy. Unlike conventional methods, our approach derives feature constraint representations via geometric operations rather than by derivative computations, and decouples the cost-function formulation, yielding a more robust and interpretable framework. Initially, a feature constraint model is formulated using the screw representation. Subsequently, a degeneracy analysis model and a judgment formula are presented, both grounded in screw theory and the feature constraint model. The proposed model inherently separates translational and helical degeneracy while accurately estimating environmental parameters, such as the position and pitch of the rotation axis, to reduce assessment errors, enable outlier rejection, and improve the robustness of degeneracy detection. A novel feature extraction and management method is proposed to improve computation efficiency and coverage of the feature in sparsely scanned scenarios. Simulations and real-world experiments show that our approach improves the robustness and accuracy of degeneracy detection while reducing the computation time by more than 70%. A dimensionless stability metric for the judgment formula is proposed, showing that our approach significantly outperforms existing state-of-the-art (SOTA) approaches by more than two orders of magnitude. To date, this study constitutes the first formal endeavor to conceptualize and systematically analyze the problem of helical degeneracy, providing a novel and rigorous perspective on the inherent challenges of robotic state estimation.

COMFI (human–robot Collaboration Oriented Markerless For Industry) is a multimodal dataset designed to advance markerless motion capture, ergonomics, and Human–Robot Collaboration (HRC) in factory settings. COMFI contains 5.4 h of synchronized and spatially co-registered streams acquired from 18 participants performing 24 tasks that span everyday movements (e.g., walking, sit-to-stand) and ergonomically demanding industrial tasks (lifting, overhead work, screwdriving, polishing, welding), with the addition of two HRC scenarios in which a Franka Emika Panda is guided by the human while holding a tool. For a total of 86.5 GB of data, it includes: calibrated multi-view RGB videos (40 Hz), optical motion capture markers and joint centers positions, as well as joint angles (100 and 40 Hz), 6D ground reaction forces (1000 and 40 Hz), and robot telemetry (200 and 40 Hz). Camera intrinsics/extrinsics, global triggers, and software-barrier synchronization for webcams are distributed, along with participant-scaled human Universal Robot Description Files that adhere to International Society of Biomechanics conventions, enabling kinematics, dynamics, and torque estimation. Videos are anonymized while preserving facial cues useful to markerless pipelines. Accompanying code supports loading, calibration, and visualization. COMFI enables rigorous benchmarking of markerless pose estimation under occlusion and clutter against reference systems, allowing the extension of current state-of-the-art algorithms to complex industrial scenarios. COMFI is expected to catalyze reproducible, cross-disciplinary research toward safer, more ergonomic HRC. For a more illustrative summary, go to https://comfi-gepetto.github.io/ .

Autonomous navigation in farmland is difficult because weak texture, crop occlusion, moving machinery, and illumination changes affect perception and control simultaneously. This study develops an adaptive unmanned aerial vehicle obstacle‐avoidance framework comprising a MobileViT‐XXS visual encoder, calibrated millimeter‐wave‐radar fusion, spatiotemporal trajectory prediction, an RRT* global planner, and a Soft Actor–Critic (SAC) local controller with semantic safety constraints. Under the matched‐SAC simulation protocol, the proposed perception backbone achieved an average mAP of 87.4%, exceeding YOLOv8n + SAC by 5.8 percentage points and EfficientViT‐L2 + SAC by 3.2 percentage points; the paired scene‐level difference relative to EfficientViT‐L2 + SAC was 3.2 percentage points (95% CI, 1.1–5.3; Holm‐adjusted p = 0.006). The complete simulation framework yielded an obstacle‐avoidance success rate of 88.3%, an average path length of 138.4 m, an energy consumption of 14.2 Wh/km, and a path smoothness of 0.21 rad/m. The matched simulation success‐rate difference relative to YOLOv8n + SAC was 4.8 percentage points (95% CI, −1.2 to 10.8; Holm‐adjusted p = 0.112), and therefore does not establish statistical superiority. Separately, the expanded field ledger contains 100 independent test flights across six scenario groups, below the prespecified target of at least 160; the field evidence is therefore reported descriptively. At 1920 × 1080 resolution, end‐to‐end latency was 49.2 ± 2.5 ms, whereas 3840 × 2160 required 92.1 ± 3.8 ms. A 60‐min 1080p sustained‐load test triggered thermal throttling at approximately 20 min and increased latency from 48.5 to 56.3 ms by 60 min. Accordingly, 1080p is the default closed‐loop mode and 4K is restricted to low‐speed inspection and ablation. The joint reward heatmaps and structured failure log further delimit the tested operating envelope. The results support technical feasibility under the evaluated conditions but not broad agricultural generalization or deployment readiness.

Enabling quadrupedal robots to traverse complex terrains, from rugged outdoor environments to urban landscapes, requires seamless integration of multiple motor skills, smooth transitions between gaits, and high-speed perceptive locomotion using only onboard sensors. We present APT-RL (action pretrained transformer–based reinforcement learning), a unified framework that enables multiskill locomotion to achieve high-speed traversal in complex environments through autonomous skill transitions using only onboard perception and computation. Our approach generates large-scale, feature-rich two-dimensional (2D) motion datasets through trajectory optimization with simplified dynamics. These datasets enable training of diverse, reusable locomotion skills that transfer effectively to a real quadruped robot operating on complex uneven terrains. The resulting high-quality skills serve as strong priors for efficient learning of complex downstream tasks and extend naturally to 3D environments, enabling smooth, high-speed multiskill locomotion in deployed policy. Real-world experiments demonstrate the framework’s capabilities: The robot performed agile maneuvers through complex indoor obstacles and outdoor wild environments, including dynamic drop-down maneuvers that reached instantaneous peak speeds of up to 6 meters per second. A single onboard policy enabled robust traversal of diverse obstacles, including stairs, hurdles, stepping stones, gaps, and fallen branches, demonstrating the versatility and effectiveness of our approach.

The human brain can process spatial action through either egocentric (self-centered) or allocentric (environment-centered) perspectives. Despite this cognitive ability, teleoperation of robotic systems has been predominantly egocentric, wherein operators control a single robot from an inside-out perspective with limited situational overview. This paper proposes a scalable paradigm for simultaneously teleoperating multiple robotic systems from an allocentric perspective that enables a single operator to solve complex collaborative tasks. The perceptual information of all robots is fused into a joint virtual environment, which the operator can view from a top-down perspective to control the robots similar to puppets. We investigated differences between egocentric and allocentric multirobot teleoperation using both simulated and real robots. In the simulation study, study participants ( n = 15) reported a higher sense of embodiment over robot bodies when using egocentric control, enabling them to perform more precise manipulation after initial training. Using allocentric control, participants achieved speed increases of 84% in a parallelizable locomotion task and 50% in a situational overview task compared with egocentric control. Participants reported improved system usability and overview with allocentric control, and no significant differences in sense of agency were measured. These results were validated in a real-world study with two physical robots ( n = 6) and underscore the effectiveness of our allocentric teleoperation framework as a complementary approach to egocentric robotic teleoperation. Interfaces that support smooth switching between allocentric and egocentric control therefore enable operators to leverage the advantages of both paradigms depending on their specific task.

Wall‐climbing robots are increasingly used in hazardous, hard‐to‐reach environments for tasks such as crack detection, hull welding, and exterior surface cleaning. As a result, the development of wall‐climbing robots capable of robust environmental adaptation, reliable adhesion, and autonomous motion control has become an important research focus. This review first examines the environmental adaptability of wall‐climbing robots, highlighting their capabilities in wall‐to‐wall transitions, navigation across curved and irregular surfaces, and obstacle negotiation, all of which are critical to enhancing operational efficiency and reliability. Subsequently, the adhesion technologies employed by wall‐climbing robots are reviewed, with particular emphasis on negative‐pressure, magnetic, and bio‐inspired adhesion mechanisms. A comparative analysis of adhesion principles, performance metrics, and load‐bearing capabilities reported in existing studies is presented to identify suitable adhesion solutions for different operating environments and to improve stability during locomotion. Finally, critical challenges and research gaps are highlighted, and a systematic discussion is provided on the relationships between locomotion modes and adhesion mechanisms, as well as between autonomy and system maturity.

People with physical disabilities face significant mobility challenges while climbing uneven surfaces and staircases. In these circumstances, traditional wheelchairs do not provide stable, safely controlled mobility, underscoring the importance of building superior stair‐climbing wheelchairs for both medical and infrastructure purposes. In this study, a review is proposed that provides a comprehensive summary of wheelchair types, developments, compares and evaluates existing stair‐climbing wheelchair technologies, and highlights major design gaps, performance limitations, and future research directions. The review examines four fundamental categories of existing systems: tracked‐based mechanisms, wheel‐leg hybrid mechanisms, tri‐wheel mechanisms, and automatic stair‐climbing systems. These mechanisms are evaluated based on critical performance metrics, including safety and stability, ergonomics, ease of use, energy efficiency, load‐carrying capacity, cost, effectiveness, adaptability to variable terrain, stair‐climbing mobility performance, and control methodologies ranging from manual to AI‐enabled modes. The key findings indicate that although significant advancements in stair‐climbing wheelchairs have been made, current systems still face challenges in portability, weight, cost, and comfort, while recent trends highlight the growing adoption of lightweight materials, hybrid locomotion, and AI‐based navigation frameworks. The proposed review has identified research gaps, including insufficient user‐centric design, high demand for low‐cost, reliable real‐world solutions, and the need for standardized safety testing methods for stair‐climbing systems. Overall, this review will provide an overview of the different technologies and a comprehensive comparison, highlight the major design trade‐offs, and suggest future research directions to advance the design and support designers, rehabilitation engineers, policymakers, and the mobility assistance technology research community in developing user‐friendly, comfortable stair‐climbing wheelchairs.

A wide‐area seabed survey was conducted using autonomous underwater vehicles (AUVs) in a known dumpsite off the coast of Southern California that has been found to have widespread sediment contamination from the chemical dichlorodiphenyltrichloroethane (DDT). The AUVs were equipped with synthetic Aperture Sonar (SAS), an electronic still camera (ESC), and a laser line scanner to characterize fine‐scale spatial variations of the surficial sediments to gain insight into their mobility. Surveys covered of the known dumpsites to assess sediment texture and distribution patterns. In parallel, a remotely operated vehicle (ROV) performed targeted optical surveys in areas identified by the AUV. The ROV collected 300 h of high‐definition video footage, providing more detailed seafloor observations that were not captured with the ESC. This study employs machine learning and seafloor roughness characterization techniques on SAS data that are informed by the other modalities of seabed sensing. The physics‐informed techniques are applied for seafloor texture classification and provide new insights for interpreting SAS imagery and the seabed in the vicinity of these dumpsites that are of emerging environmental concern.

Conventional SLAM systems using visual or LiDAR data often struggle in poor lighting and severe weather. Although 4D radar is suited for such environments, its sparse and noisy point clouds hinder accurate odometry estimation, while the radar maps suffer from obscure and incomplete structures. Thus, we propose Super4DR, a 4D radar-centric framework for learning-based odometry estimation and gaussian-based map optimization. First, we design a cluster-aware odometry network that incorporates object-level cues from the clustered radar points for inter-frame matching, alongside a hierarchical self-supervision mechanism to overcome outliers through spatio-temporal consistency, knowledge transfer, and feature contrast. Second, we propose using 3D gaussians as an intermediate representation, coupled with a radar-specific growth strategy, selective separation, and multi-view regularization, to recover blurry map areas and those undetected based on image texture. Experiments show that Super4DR achieves a 67% performance gain over prior self-supervised methods, nearly matches supervised odometry, and narrows the map quality disparity with LiDAR while enabling multi-modal image rendering.

Real-world learning can push robot manipulation beyond imitation alone.

Work environments are often inadequate and lack inclusivity for individuals with upper-body disabilities. This paper presents a novel online framework for adaptive human-robot interaction (HRI) that accommodates users' arm mobility impairments, ultimately aiming to promote active work participation. Unlike traditional human-robot collaboration approaches that assume able-bodied users, our method integrates a mobility model for specific joint limitations into a hierarchical optimal controller. This allows the robot to generate reactive, mobility-aware behaviour online and guides the user's impaired limb to exploit residual functional mobility. The framework was tested in handover tasks involving different upper-limb mobility impairments (i.e., emulated elbow and shoulder arthritis, and wrist blockage), under both standing and seated configurations with task constraints using a mobile manipulator, and complemented by quantitative and qualitative comparisons with state-of-the-art ergonomic HRI approaches. Preliminary results indicated that the framework can personalise the interaction to fit within the user's impaired range of motion and encourage joint usage based on the severity of their functional limitations.

Multi-drone payload transportation has emerged as a promising research paradigm with potential applications in construction, logistics, and disaster response. However, the complex coupled dynamics among drones, cables, and payloads pose significant challenges, and existing approaches remain limited in safety and scalability, particularly in dynamic and unstructured environments. In this work, we propose a learning-based framework for safe and scalable multi-drone cooperative payload transport. We introduce a minimal 2D abstraction that preserves the task-relevant drone-payload coupling required for coordination and safety, while remaining computationally efficient for large-scale learning. Using domain randomization over team size and physical parameters, we train a fully distributed policy via Discrete Graph Control Barrier Function Proximal Policy Optimization (DGPPO), enabling robust zero-shot sim-to-real transfer without fine-tuning. Extensive real-world evaluations demonstrate that a single learned policy generalizes across varying team sizes and task scenarios. Furthermore, multi-group hardware experiments show that the same policy can safely operate in dynamic environments, where other drone teams act as moving obstacles. These results indicate that the proposed framework enables efficient, safe, and scalable multi-drone payload transportation with strong generalization to complex real-world conditions.

The Research Article “Observing a robot peer’s failures facilitates students’ classroom learning” by L. Chen et al. had a label error in Fig. 5A. The PF group was labeled 5.74, but the correct value is 6.12. The bar height, the error bar, and the standard error annotation (±0.30) for this group were all plotted correctly. The numerical label has been corrected, and the other data and conclusions are not affected.

Reliable robot autonomy hinges on decision-making systems that account for uncertainty without imposing overly conservative restrictions on the robot’s action space. We introduce Chance-Constrained Via-Point-Based Stochastic Trajectory Optimisation ( CC - VPSTO ), a real-time capable framework for generating task-efficient robot trajectories that satisfy constraints with high probability by formulating stochastic control as a chance-constrained optimisation problem. Since such problems are generally intractable, we propose a deterministic surrogate formulation based on Monte Carlo sampling, solved efficiently with gradient-free optimisation. To address bias in naïve sampling approaches, we quantify approximation error and introduce padding strategies to improve reliability. We focus on three challenges: (i) sample-efficient constraint approximation, (ii) conditions for surrogate solution validity, and (iii) online optimisation. Integrated into a receding-horizon MPC framework, CC-VPSTO enables reactive, task-efficient control under uncertainty, balancing constraint satisfaction and performance in a principled manner. The strengths of our approach lie in its generality, that is, no assumptions on the underlying uncertainty distribution, system dynamics, cost function, or the form of inequality constraints; and its applicability to online robot motion planning. We demonstrate the validity and efficiency of our approach in both simulation and on a Franka Emika robot. Videos and additional material are made available here: https://sites.google.com/oxfordrobotics.institute/cc-vpsto .

Underwater multi‐robot and swarm systems require advanced methodologies for controlling collective behavior. Conventional single‐robot techniques, such as tethering, sonar imaging, and acoustic localization, are not scalable or effective for swarm applications. This work introduces novel approaches for visual detection, relative localization, and control of a system of multiple autonomous underwater robots. In particular, we focus on agent‐to‐agent visual localization to inform control strategies and enable the emergence of coordinated swarm behaviors. The proposed methodologies have been implemented and tested on a fleet of autonomous underwater robots developed as part of the HSURF project. We present a thorough experimental validation of the integrated detection‐to‐control system, demonstrating its robustness and effectiveness in realistic, real‐world‐like conditions. This research marks a significant step forward in developing scalable solutions for underwater multi‐robot systems.

Accurate odometry estimation is fundamental to autonomous navigation, enabling precise localization and continuous motion tracking. While vision‐based and lidar‐based odometry systems have reached a high level of maturity, their performance deteriorates significantly in adverse environmental conditions, such as low visibility, inclement weather, or dynamic lighting. In contrast, 4D radar offers inherent robustness to such challenges, along with extended range and velocity‐sensing capabilities, making it a compelling alternative for long‐term, all‐weather perception. In this paper, we present an end‐to‐end neural network‐based odometry framework that directly leverages raw ADC data from 4D radars, surpassing conventional methods that are limited to processing only 4D point cloud information. We introduce a lossless encoding scheme that converts the raw ADC signals into structured heatmap representations, preserving essential spatial and temporal information. To extract meaningful features from this rich representation, we design a hybrid attention architecture that combines intra‐frame self‐attention for enhanced spatial understanding with inter‐frame cross‐attention to model fine‐grained temporal dependencies. Comprehensive evaluations on the Coloradar dataset show that our method significantly outperforms state‐of‐the‐art radar‐based odometry approaches based on traditional feature matching or deep learning. It achieves superior accuracy and robustness, highlighting its strong potential for real‐world deployment in autonomous navigation systems under challenging conditions. Our code is available at https://github.com/MoYuGit/Deep_Radar_Odometry .

To address the limitations of traditional unmanned aerial vehicles (UAVs) and wall‐climbing robots in bridge‐pier defect detection, this research proposes a circular‐climbing robot specifically designed for bridge piers. First, based on the geometric characteristics of bridge piers and their construction environment, a circular robot frame is designed, and its mechanical behavior is analyzed to determine the conditions for stable climbing. Second, based on attitude information collected by multiple gyroscopes, an attitude estimation method for the circular robot and a multi‐wheel cooperative motion control algorithm are developed. On this basis, a multi‐motor master–slave fuzzy proportional integral derivative (PID) controller is implemented on a field programmable gate array (FPGA) platform to perform fuzzy inference on the attitude information, generate real‐time control parameters, and adaptively adjust the speeds of the crawling mechanisms, thereby achieving coordinated motion control of the multi‐wheel system. Finally, experiments are conducted to verify the system's reliability in terms of climbing stability and motion control accuracy. A comparative analysis further shows that the proposed robot exhibits a more stable mechanical structure and greater adaptability than existing wall‐climbing robots, and offers advantages in contact pressure adjustment, load capacity, and scalability, enabling it to carry various bridge pier inspection devices for different application needs. This work provides a practical platform for the automated health monitoring of bridge piers.

Robotic haircutting is an emerging application of service robotics in which safety is paramount. Unlike conventional collaborative or domestic tasks, haircutting requires prolonged operation near the human head, one of the most vulnerable body regions, and often involves sharp or heated tools. These characteristics make safety central to both functional reliability and user acceptance. This paper reviews the safety landscape of haircutting robots by examining risks across the full task workflow and synthesizing lessons from related domains, including collaborative robotics, healthcare robotics, and autonomous systems. It proposes a three‐layer framework to organize existing approaches and illustrate how safety can be embedded from hardware design to control logic. In addition, a series of proof‐of‐concept demonstrations illustrates the practical implementation of the proposed three‐layer safety framework in representative haircutting scenarios. The paper further highlights opportunities and unresolved challenges in ensuring safe human‐robot interaction in haircutting contexts, providing guidance for future research and deployment.

With the expansion of urban water supply networks and the increasing visibility of aging infrastructure, traditional manual operations and maintenance can no longer meet the demands for high precision, low disturbance, and trenchless intelligent solutions. Robotic technology is currently one of the most advanced technologies for the operation and maintenance of water supply pipeline networks, and it is steadily gaining popularity in industry research. This paper provides a systematic review of the current research status of intelligent operation and maintenance robots across three major functional areas: detection, cleaning, and repair. It describes in detail the technical principles, application advantages, and current constraints of different kinds of robots. Multi‐source sensors are used by detection robots to locate and identify defects. Cleaning robots remove dirt using mechanical, water jet, and ultrasonic technologies. Spray‐in‐place lining and UV curing techniques are used by repair robots to accomplish trenchless rehabilitation. This review contributes a functional‐performance matrix that reveals a critical gap in robots capable of both high‐traction cleaning and precise inspection, pointing to cable‐free, autonomous navigation, intelligent control, modular and reconfigurable designs as the most urgent research frontier. Provides a clear future development direction for intelligent operation and maintenance robots.

In recent years, the demand for autonomous navigation of underwater biomimetic robots in complex environments has continued to grow, among which monocular visual simultaneous localization and mapping (SLAM) has emerged as a key technology for achieving lightweight navigation and localization. However, typical underwater illumination conditions, such as low‐light and artificial lighting, significantly degrade image quality and feature observability, thereby severely limiting the robustness and accuracy of existing visual SLAM systems. To address these challenges, this paper proposes a monocular visual SLAM system for bionic robotic fish, termed GSL‐SLAM , built upon the ORB‐SLAM3 framework. The proposed system integrates adaptive underwater image enhancement with learning‐based feature extraction and matching to improve visual usability and feature matching stability under complex illumination conditions. A multi‐threaded system architecture is further adopted to ensure real‐time performance. Extensive experiments conducted on both a laboratory pool dataset and the public AquaticVision dataset demonstrate that the proposed method consistently outperforms ORB‐SLAM3 under various underwater lighting conditions, including natural light, low light, and artificial illumination. Compared with the baseline method, GSL‐SLAM achieves significant improvements in initialization efficiency, map completeness, and localization accuracy, with particularly notable advantages under low‐light and artificial lighting scenarios. Moreover, the system operates stably at approximately 20 Hz on the embedded platform, satisfying the real‐time perception and navigation requirements of the bionic robotic fish. The experimental results indicate that GSL‐SLAM substantially enhances the robustness and practicality of monocular visual SLAM in complex underwater lighting environments, providing an effective visual localization and mapping solution for lightweight autonomous underwater robots.

Compositionality, by organizing complex behavior as combinations of simpler elements, enables robot learning that is scalable and data efficient. Leveraging this principle, we propose a generative learning-from-demonstration framework that enables compositional modeling of robotic behavior by connecting perception and motion through shared object-level representations. We render scenes from object-centric neural representations that integrate canonical neural fields with latent-conditioned deformations, capturing positional and geometric variations in a smooth, consistent, and interpretable way. For motion generation, a temporal mixture-of-experts (MoE) employs a gating mechanism to combine object-conditioned movement primitives over time, producing complete trajectories. This spatial-temporal compositionality maintains the data efficiency of movement primitives while grounding motion in visual structure, enabling systematic generalization across diverse scene configurations. In simulation, long-horizon manipulation tasks are successfully completed using the proposed model, which requires significantly less training data than other image-based baselines. Real-world experiments further demonstrate the method's robustness to noise, its ability to generalize at the category level through language-based segmentation models, and its capacity to operate directly on 3D scene representations.

Autonomous multi-finger grasping is a fundamental capability in robotic manipulation. Optimization-based approaches show strong performance, but tend to be sensitive to initialization and are potentially time-consuming. As an alternative, the generator-evaluator-planner framework has been proposed. A generator generates grasp candidates, an evaluator ranks the proposed grasps, and a motion planner plans a trajectory to the highest-ranked grasp. If the planner doesn't find a trajectory, a new trajectory optimization is started with the next-best grasp as the target and so on. However, executing lower-ranked grasps means a lower chance of grasp success, and multiple trajectory optimizations are time-consuming. Alternatively, relaxing the threshold for motion planning accuracy allows for easier computation of a successful trajectory but implies lower accuracy in estimating grasp success likelihood. It's a lose-lose proposition: either spend more time finding a successful trajectory or have a worse estimate of grasp success. We propose a framework that plans trajectories to a set of generated grasp targets, the evaluator estimates the grasp success likelihood at the terminal configuration of the planned trajectories, and the robot executes the trajectory most likely to succeed. Our experiments show our approach improves over the traditional generator-evaluator-planner framework across different objects, generators, and motion planners, and successfully generalizes to novel environments in the real world, including different shelves and table heights. Project Website: https://martinmatak.github.io/fpte/

Traditional sampling methodologies that depend on the manual operation of large submersibles are characterized by significant time consumption and labor intensity. The incorporation of autonomous underwater robotic arms within unmanned underwater vehicles (UUVs) presents a promising opportunity to markedly improve sampling efficiency. This paper delineates the design of a lightweight underwater robotic arm specifically engineered for the sampling of hydrothermal fluids at the seafloor. We establish a comprehensive modeling framework that integrates kinematic, rigid‐body, and hydrodynamic parameters. To address environmental uncertainties and unmodeled dynamics, we employ a long short‐term memory (LSTM)–augmented model in conjunction with a momentum‐based observer for end‐effector force estimation, obviating the need for external force/torque sensors. Furthermore, we propose a multi‐sensor fusion control strategy that synergizes visual perception with temperature tracking and force feedback, thereby facilitating autonomous and precise sampling of hydrothermal vents. The proposed methodology has undergone experimental validation, yielding promising outcomes.

This article introduces RLNetShoot, a reinforcement learning framework for the autonomous navigation of purse seine vessels during fishing net deployment. In this method, the vessel encircles a moving fish school using a deforming cylindrical net. Encirclement success depends heavily on the shooting trajectory, which is traditionally guided by the skipper's intuition to account for ocean currents and fish evasion. However, unsuccessful attempts can occur, leading to repeated shots that waste fuel and time. RLNetShoot formulates net shooting as a Markov Decision Process and trains a Proximal Policy Optimization policy-based Reinforcement Learning agent. At each decision step, the agent receives observations about the fish-school distribution and current-induced net drift relative to the vessel and produces the required vessel steering to guide the vessel motion. RLNetShoot introduces a task-specific reward function, with the contribution of each component extensively evaluated in an ablation study carried out in a realistic, physics-aware simulation environment. RLNetShoot was compared against existing state-of-the-art solutions, achieving the highest mean catch rate in both systematic and randomized simulation tests. These results, pending ongoing validations in real practice, suggest sound adaptations to changes in the environment with the potential to reduce operational costs and generate real-time steering actions with reduced reliance on manual decision-making. The latter is particularly relevant in the sector to alleviate the challenge of the inter-generational skills shortage prevalent in the industry.

Many ceiling construction tasks still rely on heavy serial manipulators that are difficult to deploy in cluttered interiors, motivating lightweight, field-ready alternatives that reach ceiling height while maintaining millimeter-level accuracy and the stiffness demanded by overhead tool loads. We introduce Tripody, a wheeled 3-DoF parallel robot for high-reach tasks that replaces the base spherical joints of a classical 3-SPR (3 legs; S: base spherical joint; P: actuated prismatic joint; R: end-effector revolute joint) morphology with universal joints, intentionally overconstraining the mechanism; small, distributed elastic deflections absorb the resulting incompatibilities, preserving predominantly translational motion. The 33kg system extends from 1.7m to 3.4m in height, supports a continuous 32kg payload, and offers a modular end-effector interface for ceiling operations. We detail the mechanical design - including custom linear actuators and a kinematic-compatibility analysis - and a control stack for accurate positioning that combines SE(3) state estimation, forward kinematics, and task-space control. In experiments, Tripody exhibits similar in-plane stiffness to a spherical-base variant but substantially higher torsional stiffness - an increase of 67% at 1.7m, 196% at 2.6m, and 454% at 3.4m - while maintaining negligible cross-axis coupling. Closed-loop positioning with a total station converges below 0.6mm across the entire workspace; pure model extrapolation achieves a 95th-percentile error of 2.7mm (max 3.6mm). Finally, we demonstrate task-level ceiling-drilling feasibility in an open-loop study by drilling a 15-hole pattern with 4.5mm maximum relative hole-position error after rigid alignment. These results support overconstrained, compliance-absorbing 3-SPR-like architectures as a practical path to lightweight, high- reach, millimeter-accurate construction robots.

Offshore wind turbine blade repair requires stable material removal and precise force regulation under curved‐surface contact and environmental disturbance. To address these challenges, this paper proposes an integrated constant‐force grinding method that combines a passive compliant end‐effector, a particle swarm optimization‐backpropagation neural network (PSO‐BP), and an improved active disturbance rejection control (ADRC) strategy. First, a passive compliant end‐effector with variable stiffness is designed to improve contact adaptability and reduce grinding impact on curved blade surfaces. Second, a PSO‐BP model is established to predict the material removal rate (MRR) and surface roughness (Ra) under different grinding conditions, thereby providing data‐driven support for process‐state evaluation and parameter scheduling. Third, based on a controller‐oriented force‐dynamics model, an improved ADRC framework integrating a tracking differentiator, nonlinear extended state observer, nonlinear state error feedback, and PSO‐BP‐assisted gain scheduling is developed for constant‐force grinding. A Lyapunov‐based analysis shows that the closed‐loop system is uniformly ultimately bounded under bounded disturbances and bounded scheduling error. Simulation and experimental results demonstrate that, compared with PID and standard ADRC, the proposed method achieves higher force‐tracking accuracy, stronger disturbance rejection, and better grinding quality. Under equivalent initial damage conditions, it produces the lowest post‐grinding surface roughness, indicating that the proposed method provides an effective solution for offshore blade grinding repair.

With the advancement of deep‐sea resource exploration and scientific research, underwater operations are evolving from rough manipulation to precise and intelligent tasks, imposing higher demands on the dexterity and environmental adaptability of underwater grippers. However, existing reviews often focus on superficial classifications based on actuation modes or application scenarios, there by lacking in‐depth analysis grounded in robotic grasping theory and system‐level integration. This paper presents a comprehensive review of underwater dexterous grippers, proposing a novel classification framework based on the integration of force/form closure theory and energy input methods. Under this framework, we systematically analyze the structural designs and mechanisms of grippers ranging from Fully Passive to Fully Active configurations. Furthermore, the review delves into kinematic and dynamic modeling methodologies for rigid, continuum, and rigid‐continuum coupled structures, alongside the challenges and solutions for underwater tactile and deformation perception systems. We also examine control strategies, progressing from low‐level open‐loop control to model‐based intelligent approaches, including the dynamic coupling within vehicle‐manipulator‐gripper systems (UVMS). To address the lack of standardized benchmarking, this study establishes a unified evaluation system incorporating quantitative metrics (e.g., Load‐to‐Weight Ratio, Normalized Durability Index) and benchmark protocols, providing a multi‐dimensional comparative analysis of existing technologies. Finally, the paper identifies emerging trends, suggesting that bio‐inspired rigid‐soft hybrid designs, multimodal perception fusion, and hybrid modeling approaches combining Physics‐Informed Neural Networks with Computational Fluid Dynamics (PINN‐CFD) represent critical directions for achieving highly reliable and dexterous underwater manipulation.

Large‐scale heavy‐duty hexapod robots have complex structures and driving methods, and require high real‐time motion control. Due to the large computational complexity of traditional dynamics control methods, which require the application of the robot's dynamic equations, it is difficult to achieve real‐time control. Moreover, the dynamics model of large‐scale heavy‐duty hexapod robots is difficult to accurately obtain because of the structural elastic deformation, making practical application quite challenging. This article proposed a Spring‐Muscle model to simplify the dynamics model of large‐scale heavy‐duty hexapod robots with symmetrical configuration and established a force‐controlled compliant control architecture for heavy‐duty hexapod robots. First, inspired by the EPH, the dynamics model of robot legs was simplified as a Spring‐Muscle model, and we analyzed dynamic equation of large‐scale heavy‐duty hexapod robots. Second, based on deformation force coordination constraints and tangential force distribution constraints, force distribution method is proposed to establish the overall dynamics balance relationship of the robot during motion. Finally, an overall control architecture is proposed and analyzed, which achieves smooth adjustment of the robot's foot‐ground interaction process, preventing large foot‐ground contact impacts. We validate our theory through a prototype of the principle. The related findings could improve the computational efficiency of robots and enhance their motion stability.

The rising levels of water pollution worldwide, particularly in urban areas due to industrial advancements, have created an urgent need for sustainable and intelligent surface water cleaning solutions. Robotic systems have emerged as one of the promising approaches to tackle floating debris and other contaminants that affect aquatic ecosystems. This review critically examines the recent advancements in surface water cleaning robots, focusing on their structural designs, operational principles, autonomy levels, and energy efficiency. Particular emphasis is placed on the integration of guidance, navigation, and control (GNC) systems that enable semi‐autonomous and fully autonomous functionality. Existing robotic platforms vary widely in their mechanical setups and cleaning mechanisms, ranging from simple net‐based collectors to more advanced systems equipped with visual sensors and embedded control logic. However, a majority of these systems still depend on limited sensing and require periodic human intervention. The lack of advanced imaging technologies for debris detection, real‐time classification, obstacle detection and navigation restrict their autonomous potential. In addition to technical comparisons, the paper highlights the limitations and research challenges, including power management, visual processing capabilities, and adaptive path planning. The need for more intelligent, low‐power, and modular systems is emphasized, particularly those capable of handling diverse aquatic environments with minimal supervision. By consolidating current knowledge and identifying performance gaps, this review aims to guide future research efforts in the development of efficient, scalable, and environmentally sustainable robotic systems for surface water cleaning. These advancements are essential in promoting long‐term solution for water pollution, water resource management, and supporting efforts to maintain cleaner aquatic ecosystems.

Global localization, which aims to obtain the absolute position of a planetary rover in georeferenced satellite images, is a challenging task in deep space exploration. The success of global localization is beneficial for long‐range exploration and therefore supports the emergence of significant scientific discoveries. Traditional high‐precision global localization methods usually generate the digital orthophoto map (DOM) product from the rover images in a certain place and obtain the rover position by registering the rover DOM product with the orbiter satellite images. This operation is commonly accomplished offline after the rover images are transmitted to the ground stations, due to the limited computational resources of the rover's online system. However, this procedure is time‐consuming and therefore impedes the operational efficiency of rovers. Moreover, the global localization operation is not always available owing to the difficulties in rover‐satellite image matching for extraterrestrial surfaces with poor textures. In this paper, an active global localization framework of planetary rover guided by the interest region perception is proposed, which is distinctly different from the commonly used passive mode. The main novelty of the proposed method is that the global localization module will only be activated automatically when recognizing certain interest regions which are helpful for the rover‐satellite image matching tasks. In this way, the efficiency of the global localization procedure can be improved greatly and the success rate can also be ensured. The described framework has been validated with the camera images from China's first Mars rover (Zhurong), and it is observed that the run time of the global localization operation can be reduced by 70% compared with the traditional processing average, indicating its potential for online application in global localization of rovers.

In unstructured environments, background regions and target objects often share visual attributes, such as texture, color, and structure. This similarity easily leads to feature confusion, posing significant challenges for grasp pose estimation. To address this issue, we propose BF‐GNet (RGB‐D fusion network for grasp pose estimation in complex background environments), an RGB‐D fusion network designed for grasp detection under complex backgrounds. At its core lies a scene‐aware Bimodal Foreground Enhancement Module, which employs a multilayer perceptron–based controller to adaptively weight RGB and depth modalities according to the input scene: prioritizing depth information in texture‐deficient conditions and relying more on RGB semantic cues when geometry is ambiguous. This adaptive fusion strategy effectively emphasizes salient foreground grasp regions while suppressing background interference. In addition, BF‐GNet integrates a Feature‐Channel‐Sharing 2D Selective Scan (FCSS) Module based on State Space Models, enabling long‐range dependency modeling with linear complexity. Experimental results show that BF‐GNet achieves accuracies of 98.9% and 94.8% on the Cornell and Jacquard data sets, and 95.12% and 91.8% on the cluttered CBRGD and large‐scale GraspNet‐1Billion data sets, respectively. Meanwhile, its computational cost remains as low as 0.72 GFLOPs, demonstrating strong competitiveness against existing methods. Furthermore, real‐world robotic experiments yield an average grasp success rate of 90.50%, validating the robustness, generalization capability, and practical value of BF‐GNet in complex scenarios. The video is available at https://youtube.com/shorts/eyLFwb3K-UE?si=B5U9xzXCt5YliGvB .

Robust localization of autonomous underwater vehicles is conventionally performed using dead‐reckoning, but its accumulated error can be excessive for long‐term or pioneering exploration when the Doppler Velocity Log cannot maintain bottom‐lock and when positioning references such as Ultra‐Short‐Base‐Line systems are unavailable. In this letter, we present an online localization that identifies current disturbances and corrects underwater position in real time without global references. An online Pruned Exact Linear Time method for optimal detection of statistical anomalies, such as current disturbances, is further expanded for real‐time dataset update and integrated with Kalman filter state estimation via recursive iteration. We demonstrate high accuracy in random‐current simulation and validate localization performance through implementation using 3‐h field data at two offshore sites. Furthermore, the comparative analysis indicates that, unlike other methods, online localization can effectively estimate underwater position in the presence of varying or stronger currents without dynamic model constraints.

Rebar node tying on construction sites currently relies heavily on manual labor. The few existing mobile robots developed for this task are hindered by low end‐effector efficiency and poor motion continuity, characterized by redundant movements and travel paths. To address these issues, this article proposes a trajectory collaborative optimization strategy for the end‐effector of a rebar tying robot. The strategy optimizes the operational sequence of the end‐effector's horizontal, rotational, and descending modules, shortens their travel trajectories, and enhances the motion coupling between them. By establishing a pipeline parallel anticipation mechanism based on characteristic height layers, the strategy transforms the discrete “start‐stop” operational paradigm into a synchronized, continuous coupling framework. The performance of the proposed strategy was validated through comparative virtual simulations and physical experiments against an industrial‐standard Greedy Nearest Neighbor (GNN) baseline. The results demonstrate that for a standard eight‐node tying task, the strategy reduces the cumulative rotation angle by 46.2% and operational movements by 61.1%, while the total travel path is shortened by 43.3%. Most notably, the core system time is compressed by 38.0%, leading to a 136.5% improvement in overall operational efficiency compared with the non‐coupled baseline. The study further identifies a divergent performance gap, proving that the strategy's synergistic advantages—driven by the motion‐masking effect—become increasingly prominent in sparse task distributions. This research provides a robust, feasible solution for multi‐target sequencing and trajectory optimization in unstructured, large‐scale construction environments.