Planning in environments with moving obstacles remains a significant challenge in robotics. While many works focus on navigation and path planning in obstacle-dense spaces, traversing such congested regions is often avoidable by selecting alternative routes. This paper presents Traversability-aware FMM (Tr-FMM), a path planning method that computes paths in dynamic environments, avoiding crowded regions. The method operates in two steps: first, it discretizes the environment, identifying regions and their distribution; second, it computes the traversability of regions, aiming to minimize both obstacle risks and goal deviation. The path is then computed by propagating the wavefront through regions with higher traversability. Simulated and real-world experiments demonstrate that the approach enhances significant safety by keeping the robot away from regions with obstacles while reducing unnecessary deviations from the goal.
Wind hazards such as tornadoes and straight-line winds frequently affect vulnerable communities in the Great Plains of the United States, where limited infrastructure and sparse data coverage hinder effective emergency response. Existing forecasting systems focus primarily on meteorological elements and often fail to capture community-specific vulnerabilities, limiting their utility for localized risk assessment and resilience planning. To address this gap, we propose an interpretable dual-stream learning framework that integrates structured numerical weather data with unstructured textual event narratives. Our architecture combines a Random Forest and RoBERTa-based transformer through a late fusion mechanism, enabling robust and context-aware wind hazard prediction. The system is tailored for underserved tribal communities and supports block-level risk assessment. Experimental results show significant performance gains over traditional baselines. Furthermore, gradient-based sensitivity and ablation studies provide insight into the model's decision-making process, enhancing transparency and operational trust. The findings demonstrate both predictive effectiveness and practical value in supporting emergency preparedness and advancing community resilience.
Background: Deep learning has potential to improve the efficiency and consistency of radiation therapy planning, but clinical adoption is hindered by the limited model generalizability due to data scarcity and heterogeneity among institutions. Although aggregating data from different institutions could alleviate this problem, data sharing is a practical challenge due to concerns about patient data privacy and other technical obstacles. Purpose: This work aims to address this dilemma by developing FedKBP+, a comprehensive federated learning (FL) platform for predictive tasks in real-world applications in radiotherapy treatment planning. Methods: We implemented a unified communication stack based on Google Remote Procedure Call (gRPC) to support communication between participants whether located on the same workstation or distributed across multiple workstations. In addition to supporting the centralized FL strategies commonly available in existing open-source frameworks, FedKBP+ also provides a fully decentralized FL model where participants directly exchange model weights to each other through Peer-to-Peer communication. We evaluated FedKBP+ on three predictive tasks using scale-attention network (SA-Net) as the predictive model. Conclusions: Our results demonstrate that FedKBP+ is highly effective, efficient and robust, showing great potential as a federated learning platform for radiation therapy.
In the field of urban planning, existing Vision-Language Models (VLMs) frequently fail to effectively analyze and evaluate planning maps, despite the critical importance of these visual elements for urban planners and related educational contexts. Planning maps, which visualize land use, infrastructure layouts, and functional zoning, require specialized understanding of spatial configurations, regulatory requirements, and multi-scale analysis. To address this challenge, we introduce PlanGPT-VL, the first domain-specific Vision-Language Model tailored specifically for urban planning maps. PlanGPT-VL employs three innovative approaches: (1) PlanAnno-V framework for high-quality VQA data synthesis, (2) Critical Point Thinking to reduce hallucinations through structured verification, and (3) comprehensive training methodology combining Supervised Fine-Tuning with frozen vision encoder parameters. Through systematic evaluation on our proposed PlanBench-V benchmark, we demonstrate that PlanGPT-VL significantly outperforms general-purpose state-of-the-art VLMs in specialized planning map interpretation tasks, offering urban planning professionals a reliable tool for map analysis, assessment, and educational applications while maintaining high factual accuracy. Our lightweight 7B parameter model achieves comparable performance to models exceeding 72B parameters, demonstrating efficient domain specialization without sacrificing performance.
The long term consequences of unwanted pregnancies carried to term on mothers have not been much explored. We use data from the Wisconsin Longitudinal Study (WLS) and propose a novel approach, namely two team cross-screening, to study the possible effects of unwanted pregnancies carried to term on various aspects of mothers' later-life mental health, physical health, economic well-being and life satisfaction. Our method, unlike existing approaches to observational studies, enables the investigators to perform exploratory data analysis, confirmatory data analysis and replication in the same study. This is a valuable property when there is only a single data set available with unique strengths to perform exploratory, confirmatory and replication analysis. In two team cross-screening, the investigators split themselves into two teams and the data is split as well according to a meaningful covariate. Each team then performs exploratory data analysis on its part of the data to design an analysis plan for the other part of the data. The complete freedom of the teams in designing the analysis has the potential to generate new unanticipated hypotheses in addition to a prefixed set of hypotheses. Moreover, only the hypotheses that looked promising in the data each team explored are forwarded for analysis (thus alleviating the multiple testing problem). These advantages are demonstrated in our study of the effects of unwanted pregnancies on mothers' later life outcomes.
This paper introduces a novel semantics-aware inspection planning policy derived through deep reinforcement learning. Reflecting the fact that within autonomous informative path planning missions in unknown environments, it is often only a sparse set of objects of interest that need to be inspected, the method contributes an end-to-end policy that simultaneously performs semantic object visual inspection combined with collision-free navigation. Assuming access only to the instantaneous depth map, the associated segmentation image, the ego-centric local occupancy, and the history of past positions in the robot's neighborhood, the method demonstrates robust generalizability and successful crossing of the sim2real gap. Beyond simulations and extensive comparison studies, the approach is verified in experimental evaluations onboard a flying robot deployed in novel environments with previously unseen semantics and overall geometric configurations.
Visual-Interleaved Chain-of-Thought (VI-CoT) enables MLLMs to continually update their understanding and decisions based on step-wise intermediate visual states (IVS), much like a human would, which demonstrates impressive success in various tasks, thereby leading to emerged advancements in related benchmarks. Despite promising progress, current benchmarks provide models with relatively fixed IVS, rather than free-style IVS, whch might forcibly distort the original thinking trajectories, failing to evaluate their intrinsic reasoning capabilities. More importantly, existing benchmarks neglect to systematically explore the impact factors that IVS would impart to untamed reasoning performance. To tackle above gaps, we introduce a specialized benchmark termed ViC-Bench, consisting of four representive tasks: maze navigation, jigsaw puzzle, embodied long-horizon planning, and complex counting, where each task has dedicated free-style IVS generation pipeline supporting function calls. To systematically examine VI-CoT capability, we propose a thorough evaluation suite incorporating a progressive three-stage strategy with targeted new metrics. Besides, we establish Incremental Prompting Information Injection (IPII) strategy to ablatively explore the prompting factors for VI-CoT. We extensively conduct evaluations for 18 advanced MLLMs, revealing key insights into their VI-CoT capability. Our proposed benchmark is publicly open at Huggingface.
Context. The NewAthena mission planned for launch in the mid-2030s will carry X-IFU, an integral field unit spectrometer that will enable unique insight in the X-ray hot universe through its combination of spectral and spatial capabilities. The high spectral resolution will allow a mapping of turbulent velocities of the hot gas in galaxy clusters, providing an unrivaled way to study the complex dynamics within galaxy clusters. Aims. This is the fourth in a series of papers aimed at forecasting the ability to investigate turbulence in the intracluster medium through the observation of the centroid shift caused by turbulent motions of the gas. In this paper we improve on previous methods by investigating the ability of simulation-based inference (SBI) to constrain the underlying nature of velocity fluctuations through the use of standard observational diagnostics, such as the structure function. Methods. We rely on a complex architecture of neural networks in order to model the likelihood and posterior distributions relevant to our case. We investigate its capability to retrieve the turbulence parameters on mock observations, and explore its capability to use alternative summary statistics. Conclusions. Our trained models are able to infer the parameters of the intracluster gas velocity power-spectrum in independently simulated X-IFU observations of a galaxy cluster. We evaluated the precision of the recovery for different models. We show the necessity to use methods such as SBI to avoid an under-estimation of the sources of variance by comparing the results to our previous paper. We confirm that sample variance severely impacts the precision of recovered turbulent features. Our results demonstrate the need for advanced modeling methods to tackle the complexity of the physical information nested within future observations expected from X-IFU/NewAthena.
Despite decades of HCI and Meeting Science research, complaints about ineffective meetings are still pervasive. We argue that meeting technologies lack support for prospective reflection, that is, thinking about why a meeting is needed and what might happen. To explore this, we designed a Meeting Purpose Assistant (MPA) technology probe to coach users to articulate their meeting's purpose and challenges, and act accordingly. The MPA used Generative AI to support personalized and actionable prospective reflection across the diversity of meeting contexts. Using a participatory prompting methodology, 18 employees of a global technology company reflected with the MPA on upcoming meetings. Observed impacts were: clarifying meeting purposes, challenges, and success conditions; changing perspectives and flexibility; improving preparation and communication; and proposing changed plans. We also identify perceived social, temporal, and technological barriers to using the MPA. We present system and workflow design considerations for developing AI-assisted reflection support for meetings.
Path planning is crucial for the navigation of autonomous vehicles, yet these vehicles face challenges in complex and real-world environments. Although a global view may be provided, it is often outdated, necessitating the reliance of Unmanned Ground Vehicles (UGVs) on real-time local information. This reliance on partial information, without considering the global context, can lead to UGVs getting stuck in local minima. This paper develops a method to proactively predict local minima using Dynamic Bayesian filtering, based on the detected obstacles in the local view and the global goal. This approach aims to enhance the autonomous navigation of self-driving vehicles by allowing them to predict potential pitfalls before they get stuck, and either ask for help from a human, or re-plan an alternate trajectory.
Since its approval in 2016, NA64 has pioneered light dark matter (LDM) searches with electron, positron, muon, and hadron beams. The experiment has successfully met its primary objectives, as outlined in the EPPS input (2018), and even exceeded them, producing results that demonstrate its ability to operate in a near-background-free environment. The Physics Beyond Collider (PBC) initiative at CERN recognizes NA64's contributions as complementary and worthy of continued exploration. Its key advantage over beam-dump approaches is that the signal rate scales as the square of the coupling rather than the fourth power, reducing the required number of beam particles for the same sensitivity. To fully exploit the NA64 physics potential, an upgrade during LS3 will enable the experiment to run in background-free mode at higher SPS beam rates. Planned upgrades include: (a) improved detector hermeticity with a new veto hadron calorimeter, (b) enhanced particle identification with a synchrotron radiation detector, and (c) increased beam rates via upgraded electronics. With the recently strengthened NA64 collaboration, stable operations and timely data analysis are planned for LHC Run 4. The expected beam exposures are approximately 1e13 electrons, 1e11 positrons (at 40 and 60 GeV), and 2e13 muons on target. This will allow NA64 to explore new LDM parameter space, with the potential for discovery or conclusive exclusion of many well-motivated models.
This paper introduces a data-driven (i.e., model-free) approach to identify which inverter-based resources (IBRs) have dominant participation in poorly damped sub-synchronous oscillations (SSO), to get to the root cause for effective mitigation. An Enhanced Dynamic Mode Decomposition (eDMD) method is proposed that incorporates an appropriate set of observables. Based on time-synchronized data (either simulated or real) from IBR connection points, eDMD directly computes data-driven eigenvectors and participation factors to reveal the role of each IBR in poorly damped SSO. We show the improved accuracy of eDMD over conventional Dynamic Mode Decomposition (DMD) by benchmarking both against actual model-based analysis. We demonstrate this first through a synthetic example and then a case study on the IEEE 39-bus test system with 100% IBR. This data-driven, model-free method offers a powerful tool to foresee and mitigate the risk of IBR-induced SSO in planning (simulated data) and post-event analysis (real data) of SSO events.
Since the standardization of IPv6 in 1998, both versions of the Internet Protocol have coexisted in the Internet. Clients usually run algorithms such as Happy Eyeballs, to decide whether to connect to an IPv4 or IPv6 endpoint for dual-stack domains. To identify whether two addresses belong to the same device or service, researchers have proposed different forms of alias resolution techniques. Similarly, one can also form siblings of IPv4 and IPv6 addresses belonging to the same device. Traditionally, all of these approaches have focused on individual IP addresses. In this work, we propose the concept of "sibling prefixes", where we extend the definition of an IPv4-IPv6 sibling to two IP prefixe-one IPv4 prefix and its sibling IPv6 prefix. We present a technique based on large-scale DNS resolution data to identify 76k IPv4-IPv6 sibling prefixes. We find sibling prefixes to be relatively stable over time. We present SP-Tuner algorithm to tune the CIDR size of sibling prefixes and improve the perfect match siblings from 52% to 82%. For more than half of sibling prefixes, the organization names for their IPv4 and IPv6 origin ASes are identical, and 60% of all sibling prefixes have at least one of the prefixes with a valid ROV status in RPKI. Furthermore, we identify sibling prefixes in 24 hypergiant and CDN networks. Finally, we plan to regularly publish a list of sibling prefixes to be used by network operators and fellow researchers in dual-stack studies.
Delays in public transport are common, often impacting users through prolonged travel times and missed transfers. Existing solutions for handling delays remain limited; backup plans based on historical data miss opportunities for earlier arrivals, while snapshot planning accounts for current delays but not future ones. With the growing availability of live delay data, users can adjust their journeys in real-time. However, the literature lacks a framework that fully exploits this advantage for system-scale dynamic replanning. To address this, we formalise the dynamic replanning problem in public transport routing and propose two solutions: a "pull" approach, where users manually request replanning, and a novel "push" approach, where the server proactively monitors and adjusts journeys. Our experiments show that the push approach outperforms the pull approach, achieving significant speedups. The results also reveal substantial arrival time savings enabled by dynamic replanning.
African swine fever (ASF) is a highly contagious viral disease that poses a significant threat to the swine industry, requiring stringent control measures, including movement restrictions that delay pig placements, impacting business continuity. The number and economic impact of unplaced healthy animals due to control zone restrictions remains unmeasured. This study evaluates the economic and epidemiological impacts of control zone placement restrictions during simulated ASF outbreaks in U.S. commercial swine farms. We model the spread of ASF and apply the U.S. National Response Plan (NRP) alongside alternative mitigation strategies, analyzing key metrics such as the number of unplaced pigs, depopulated pigs, infected farms, and total economic losses. Our findings estimate the median number of unplaced pigs in the first year was 153,020 (IQR 27,377 to 1,307,899) under the NRP scenario. Shorter control zone durations (20 to 25 days) effectively reduce the median number of unplaced pigs by 16.7% to 33.5%, whereas longer durations (40 days) increase unplacement numbers by 32%. The median number of depopulated pigs remains broadly consistent across all durations. Expanding the infected zone (5 to 15 km) increases the median number of unplaced pigs by 53.6% to 282% while reducing depopulated pigs by 28.8% to 73.9%, respectively. Economic losses are estimated through a model that includes depopulated and unplaced animals requiring culling. We examined the situations when 5%, 12%, or 20% of unplaced pigs required culling and found that the total cost ranged from zero (no second infection) to over $800 million.
Cyberattacks on smart inverters and distributed PV are becoming an imminent threat, because of the recent well-documented vulnerabilities and attack incidents. Particularly, the long lifespan of inverter devices, users' oblivion of cybersecurity compliance, and the lack of cyber regulatory frameworks exacerbate the prospect of cyberattacks on smart inverters. As a result, this raises a question -- "do cyberattacks on smart inverters, if orchestrated on a large scale, pose a genuine threat of wide-scale instability to the power grid and energy market"? This paper provides a realistic assessment on the plausibility and impacts of wide-scale power instability caused by cyberattacks on smart inverters. We conduct an in-depth study based on the electricity market data of Australia and the knowledge of practical contingency mechanisms. Our key findings reveal: (1) Despite the possibility of disruption to the grid by cyberattacks on smart inverters, the impact is only significant under careful planning and orchestration. (2) While the grid can assure certain power system security to survive inadvertent contingency events, it is insufficient to defend against savvy attackers who can orchestrate attacks in an adversarial manner. Our data analysis of Australia's electricity grid also reveals that a relatively low percentage of distributed PV would be sufficient to launch an impactful concerted attack on the grid. Our study casts insights on robust strategies for defending the grid in the presence of cyberattacks for places with high penetration of distributed PV.
This paper investigates prior prompt engineering (pPE) in the context of reinforcement fine-tuning (RFT), where language models (LMs) are incentivized to exhibit behaviors that maximize performance through reward signals. While existing RFT research has primarily focused on algorithms, reward shaping, and data curation, the design of the prior prompt--the instructions prepended to queries during training to elicit behaviors such as step-by-step reasoning--remains underexplored. We investigate whether different pPE approaches can guide LMs to internalize distinct behaviors after RFT. Inspired by inference-time prompt engineering (iPE), we translate five representative iPE strategies--reasoning, planning, code-based reasoning, knowledge recall, and null-example utilization--into corresponding pPE approaches. We experiment with Qwen2.5-7B using each of the pPE approaches, then evaluate performance on in-domain and out-of-domain benchmarks (e.g., AIME2024, HumanEval+, and GPQA-Diamond). Our results show that all pPE-trained models surpass their iPE-prompted counterparts, with the null-example pPE approach achieving the largest average performance gain and the highest improvement on AIME2024 and GPQA-Diamond, surpassing the commonly used reasoning approach. Furthermore, by adapting a behavior-classification framework, we demonstrate that different pPE strategies instill distinct behavioral styles in the resulting models. These findings position pPE as a powerful yet understudied axis for RFT.
Knowledge components (KCs) are the fundamental units of knowledge in the field of education. A KC graph illustrates the relationships and dependencies between KCs. An accurate KC graph can assist educators in identifying the root causes of learners' poor performance on specific KCs, thereby enabling targeted instructional interventions. To achieve this, we have developed a KC graph structure learning algorithm, named MAS-KCL, which employs a multi-agent system driven by large language models for adaptive modification and optimization of the KC graph. Additionally, a bidirectional feedback mechanism is integrated into the algorithm, where AI agents leverage this mechanism to assess the value of edges within the KC graph and adjust the distribution of generation probabilities for different edges, thereby accelerating the efficiency of structure learning. We applied the proposed algorithm to 5 synthetic datasets and 4 real-world educational datasets, and experimental results validate its effectiveness in learning path recognition. By accurately identifying learners' learning paths, teachers are able to design more comprehensive learning plans, enabling learners to achieve their educational goals more effectively, thus promoting the sustainable development of education.
Generating large-scale multi-character interactions is a challenging and important task in character animation. Multi-character interactions involve not only natural interactive motions but also characters coordinated with each other for transition. For example, a dance scenario involves characters dancing with partners and also characters coordinated to new partners based on spatial and temporal observations. We term such transitions as coordinated interactions and decompose them into interaction synthesis and transition planning. Previous methods of single-character animation do not consider interactions that are critical for multiple characters. Deep-learning-based interaction synthesis usually focuses on two characters and does not consider transition planning. Optimization-based interaction synthesis relies on manually designing objective functions that may not generalize well. While crowd simulation involves more characters, their interactions are sparse and passive. We identify two challenges to multi-character interaction synthesis, including the lack of data and the planning of transitions among close and dense interactions. Existing datasets either do not have multiple characters or do not have close and dense interactions. The planning of transitions for multi-character close and dense interactions needs both spatial and temporal considerations. We propose a conditional generative pipeline comprising a coordinatable multi-character interaction space for interaction synthesis and a transition planning network for coordinations. Our experiments demonstrate the effectiveness of our proposed pipeline for multicharacter interaction synthesis and the applications facilitated by our method show the scalability and transferability.
Scenario-based testing using simulations is a cornerstone of Autonomous Vehicles (AVs) software validation. So far, developers needed to choose between low-fidelity 2D simulators to explore the scenario space efficiently, and high-fidelity 3D simulators to study relevant scenarios in more detail, thus reducing testing costs while mitigating the sim-to-real gap. This paper presents a novel framework that leverages multi-agent co-simulation and procedural scenario generation to support scenario-based testing across low- and high-fidelity simulators for the development of motion planning algorithms. Our framework limits the effort required to transition scenarios between simulators and automates experiment execution, trajectory analysis, and visualization. Experiments with a reference motion planner show that our framework uncovers discrepancies between the planner's intended and actual behavior, thus exposing weaknesses in planning assumptions under more realistic conditions. Our framework is available at: https://github.com/TUM-AVS/MultiDrive
The transition to hydrogen powered transportation requires regionally tailored yet scalable infrastructure planning. This study presents the first Texas specific, multi-period mixed integer optimization model for hydrogen transportation from 2025 to 2050, addressing challenges in infrastructure phasing, asset coordination, and multimodal logistics. The framework introduces three innovations: (1) phased deployment with delayed investment constraints, (2) dynamic modeling of fleet aging and replacement, and (3) a clustering-based hub structure enabling adaptive two-stage hydrogen delivery. Simulations show pipeline deployment supports up to 94.8% of hydrogen flow by 2050 under high demand, reducing transport costs by 23% compared to vehicle-based systems. However, one-year construction delays reduce pipeline coverage by over 60%, shifting reliance to costlier road transport. While the study focuses on Texas, its modular design and adaptable inputs apply to other regions. It provides a tool for policy makers and stakeholders to manage hydrogen transitions under logistical and economic constraints.
Pulmonary segment segmentation is crucial for cancer localization and surgical planning. However, the pixel-wise annotation of pulmonary segments is laborious, as the boundaries between segments are indistinguishable in medical images. To this end, we propose a weakly supervised learning (WSL) method, termed Anatomy-Hierarchy Supervised Learning (AHSL), which consults the precise clinical anatomical definition of pulmonary segments to perform pulmonary segment segmentation. Since pulmonary segments reside within the lobes and are determined by the bronchovascular tree, i.e., artery, airway and vein, the design of the loss function is founded on two principles. First, segment-level labels are utilized to directly supervise the output of the pulmonary segments, ensuring that they accurately encompass the appropriate bronchovascular tree. Second, lobe-level supervision indirectly oversees the pulmonary segment, ensuring their inclusion within the corresponding lobe. Besides, we introduce a two-stage segmentation strategy that incorporates bronchovascular priori information. Furthermore, a consistency loss is proposed to enhance the smoothness of segment boundaries, along with an evaluation metric designed to measure the smoothness of pulmonary segment boundaries. Visual inspection and evaluation metrics from experiments conducted on a private dataset demonstrate the effectiveness of our method.
Manipulating deformable linear objects (DLOs) is challenging due to their complex dynamics and the need for safe interaction in contact-rich environments. Most existing models focus on shape prediction alone and fail to account for contact and tension constraints, which can lead to damage to both the DLO and the robot. In this work, we propose a certifiably safe motion planning and control framework for DLO manipulation. At the core of our method is a predictive model that jointly estimates the DLO's future shape and tension. These predictions are integrated into a real-time trajectory optimizer based on polynomial zonotopes, allowing us to enforce safety constraints throughout the execution. We evaluate our framework on a simulated wire harness assembly task using a 7-DOF robotic arm. Compared to state-of-the-art methods, our approach achieves a higher task success rate while avoiding all safety violations. The results demonstrate that our method enables robust and safe DLO manipulation in contact-rich environments.
Achieving coordinated teamwork among legged robots requires both fine-grained locomotion control and long-horizon strategic decision-making. Robot soccer offers a compelling testbed for this challenge, combining dynamic, competitive, and multi-agent interactions. In this work, we present a hierarchical multi-agent reinforcement learning (MARL) framework that enables fully autonomous and decentralized quadruped robot soccer. First, a set of highly dynamic low-level skills is trained for legged locomotion and ball manipulation, such as walking, dribbling, and kicking. On top of these, a high-level strategic planning policy is trained with Multi-Agent Proximal Policy Optimization (MAPPO) via Fictitious Self-Play (FSP). This learning framework allows agents to adapt to diverse opponent strategies and gives rise to sophisticated team behaviors, including coordinated passing, interception, and dynamic role allocation. With an extensive ablation study, the proposed learning method shows significant advantages in the cooperative and competitive multi-agent soccer game. We deploy the learned policies to real quadruped robots relying solely on onboard proprioception and decentralized localization, with the resulting system supporting autonomous robot-robot and robot-human soccer matches on indoor and outdoor soccer courts.
The sequence form, owing to its compact and holistic strategy representation, has demonstrated significant efficiency in computing normal-form perfect equilibria for two-player extensive-form games with perfect recall. Nevertheless, the examination of $n$-player games remains underexplored. To tackle this challenge, we present a sequence-form characterization of normal-form perfect equilibria for $n$-player extensive-form games, achieved through a class of perturbed games formulated in sequence form. Based on this characterization, we develop a differentiable path-following method for computing normal-form perfect equilibria and prove its convergence. This method involves constructing an artificial logarithmic-barrier game in sequence form, where an additional variable is incorporated to regulate the influence of logarithmic-barrier terms to the payoff functions, as well as the transition of the strategy space. We prove the existence of a smooth equilibrium path defined by the artificial game, starting from an arbitrary positive realization plan and converging to a normal-form perfect equilibrium of the original game as the additional variable approaches zero. Furthermore, we extend Harsanyi's linear and logarithmic tracing procedures to the sequence form and develop two alternative methods for computing normal-form perfect equilibria. Numerical experiments further substantiate the effectiveness and efficiency of our methods.
With the planned launch of the PLAnetary Transit and Oscillation of stars (PLATO) satellite mission in 2026, an understanding of the stellar properties and spatial distribution of astrophysical false positives (FPs) is essential to ensure the limited ground-based spectroscopy resources are used efficiently to target the most likely genuine planetary transit (PT) candidates. In our previous paper, Bray et al. (2023), we presented the expected blended eclipsing binary false positive (BEB) percentage in the proposed Southern PLATO field, which we referred to as SPF0. This was obtained from a detailed statistical analysis of a complete synthetic rendering of SPF0. In this follow-up paper, we present a more detailed analysis of the synthetic binary population creating these BEBs including the apparent radii of the planets mimicked, distances from Earth, orbital periods and binary component masses and evolutionary state.We examine the properties of the BEBs from 400 new independent simulations where random orbital inclinations were assigned to all synthetic binaries in the entire SPF0 region. We consider BEBs created by all eclipses of non-compact remnant binaries. Finally we examine the BEBs most likely to be mistaken for planets in the habitable zone which we define as fully or partially eclipsing binaries with periods between 180 and 1,000 days satisfying our critical inclination angle check.
The paper presents a novel sample-based algorithm, called C*, for real-time coverage path planning (CPP) of unknown environments. The C* algorithm is built upon the concept of Rapidly Covering Graph (RCGs). The RCG is constructed incrementally via progressive sampling during robot navigation, which eliminates the need for cellular decomposition of the search space. The RCG has a sparse-graph structure formed by efficient sampling and pruning techniques, which produces non-myopic waypoints of the coverage trajectory. While C* produces the desired back and forth coverage pattern, it adapts to the TSP-based locally optimal coverage of small uncovered regions, called coverage holes, that are surrounded by obstacles and covered regions. Thus, C* proactively detects and covers the coverage holes in situ, which reduces the coverage time by preventing the longer return trajectories from distant regions to cover such holes later. The algorithmic simplicity and low computational complexity of C* makes it easy to implement and suitable for real-time onboard applications. It is analytically proven that C* provides complete coverage of unknown environments. The performance of C* is validated by 1) extensive high-fidelity simulations and 2) real laboratory experiments using autonomous robots. A comparative evaluation with seven existing CPP methods demonstrate that C* yields significant performance improvements in terms of coverage time, number of turns, trajectory length and overlap ratio, while preventing the formation of coverage holes. Finally, C* is evaluated on two different applications of CPP using 1) energy-constrained robots and 2) multi-robot teams.
This paper presents the development of a wearable ankle rehabilitation robot based on a 3-RRR spherical parallel mechanism (SPM) to support multi-DOF recovery through pitch, roll, and yaw motions. The system features a compact, ergonomic structure designed for comfort, safety, and compatibility with ankle biomechanics. A complete design-to-dynamics pipeline has been implemented, including structural design, kinematic modeling for motion planning, and Lagrangian-based dynamic modeling for torque estimation and simulation analysis. Preliminary simulations verify stable joint coordination and smooth motion tracking under representative rehabilitation trajectories. The control framework is currently being developed to enhance responsiveness across the workspace. Future work will focus on integrating personalized modeling and adaptive strategies to address kinematic singularities through model based control. This work establishes a foundational platform for intelligent, personalized ankle rehabilitation, enabling both static training and potential extension to gait-phase-timed assistance.
Collision-free planning is essential for bipedal robots operating within unstructured environments. This paper presents a real-time Model Predictive Control (MPC) framework that addresses both body and foot avoidance for dynamic bipedal robots. Our contribution is two-fold: we introduce (1) a novel formulation for adjusting step timing to facilitate faster body avoidance and (2) a novel 3D foot-avoidance formulation that implicitly selects swing trajectories and footholds that either steps over or navigate around obstacles with awareness of Center of Mass (COM) dynamics. We achieve body avoidance by applying a half-space relaxation of the safe region but introduce a switching heuristic based on tracking error to detect a need to change foot-timing schedules. To enable foot avoidance and viable landing footholds on all sides of foot-level obstacles, we decompose the non-convex safe region on the ground into several convex polygons and use Mixed-Integer Quadratic Programming to determine the optimal candidate. We found that introducing a soft minimum-travel-distance constraint is effective in preventing the MPC from being trapped in local minima that can stall half-space relaxation methods behind obstacles. We demonstrated the proposed algorithms on multibody simulations on the bipedal robot platforms, Cassie and Digit, as well as hardware experiments on Digit.
Many animals possess a remarkable capacity to rapidly construct flexible mental models of their environments. These world models are crucial for ethologically relevant behaviors such as navigation, exploration, and planning. The ability to form episodic memories and make inferences based on these sparse experiences is believed to underpin the efficiency and adaptability of these models in the brain. Here, we ask: Can a neural network learn to construct a spatial model of its surroundings from sparse and disjoint episodic memories? We formulate the problem in a simulated world and propose a novel framework, the Episodic Spatial World Model (ESWM), as a potential answer. We show that ESWM is highly sample-efficient, requiring minimal observations to construct a robust representation of the environment. It is also inherently adaptive, allowing for rapid updates when the environment changes. In addition, we demonstrate that ESWM readily enables near-optimal strategies for exploring novel environments and navigating between arbitrary points, all without the need for additional training.