Kinodynamic motion planning for a multirobot system with different dynamics and actuation limits is a challenging problem. The difficulty increases with the presence of aerodynamic interaction forces that occur when aerial robots fly in close proximity. Due to these complexities, existing planners either rely on simplified assumptions (like ignoring robot dynamics and interaction forces) or produce highly suboptimal solutions. This article presents a kinodynamic motion planner for a heterogeneous team of robots that respects robot dynamics, scales well to 16 robots, and directly reasons about interaction forces between aerial robots operating in close proximity. Our method, db-ECBS, generalizes the multiagent path-finding method Enhanced Conflict-Based Search (ECBS) to the continuous domain by using the single-robot kinodynamic motion planner discontinuity-bounded A. The planner db-ECBS operates on three levels. Initially, individual robot trajectories are computed using a graph search that allows bounded discontinuities between precomputed motion primitives. The second level identifies interrobot collisions or interaction force violations and resolves them by imposing constraints on the first level. The third and final level uses the resulting solution with discontinuities as an initial guess for a joint-space trajectory optimization. The procedure is repeated with a reduced discontinuity bound, resulting in an anytime, probabilistically complete, and asymptotically bounded suboptimal planner. We provide a benchmark of 65 problems with six different dynamics. We demonstrate that db-ECBS produces trajectories that are less than half the cost of existing planners. We show that the interaction-awareness is particularly important for very dense scenarios.
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CODEI: Resource-Efficient Task-Driven Co-Design of Perception and Decision Making for Mobile Robots Applied to Autonomous Vehicles (Abstract Reprint)
PDF ↗This article discusses the integration challenges and strategies for designing mobile robots, by focusing on the task-driven, optimal selection of hardware and software to balance safety, efficiency, and minimal usage of resources such as costs, energy, computational requirements, and weight. We emphasize the interplay between perception and motion planning in decision-making by introducing the concept of occupancy queries to quantify the perception requirements for sampling-based motion planners. Sensor and algorithm performance are evaluated using false negative rate and false positive rate across various factors such as geometric relationships, object properties, sensor resolution, and environmental conditions. By integrating perception requirements with perception performance, an integer linear programming approach is proposed for efficient sensor and algorithm selection and placement. This forms the basis for a co-design optimization that includes the robot body, motion planner, perception pipeline, and computing unit. We refer to this framework for solving the co-design problem of mobile robots as CODEI, short for co-design of embodied intelligence. A case study on developing an autonomous vehicle for urban scenarios provides actionable information for designers, and shows that complex tasks escalate resource demands, with task performance affecting choices of the autonomy stack. The study demonstrates that resource prioritization influences sensor choice: cameras are preferred for cost-effective and lightweight designs, while lidar sensors are chosen for better energy and computational efficiency.
Hierarchical Task Network (HTN) planners generate plans using a decomposition process with extra domain knowledge to guide search towards a planning task. Domain experts develop such domain knowledge through recipes of how to decompose higher level tasks, specifying which tasks can be decomposed and under what conditions. In most realistic domains, such recipes contain recursions, i.e., tasks that can be decomposed into other tasks that contain the original task. Such domains require that either the domain expert tailor such domain knowledge to the specific HTN planning algorithm, or an algorithm that can search efficiently using such domain knowledge. By leveraging a three-stage compiler design we can easily support more language descriptions and preprocessing optimizations that when chained can greatly improve runtime efficiency in such domains. In this paper we evaluate such optimizations with the HyperTensioN HTN planner, winner of the HTN IPC 2020 total-order track.
Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models (Abstract Reprint)
PDF ↗The performance of optimization-based robot motion planning algorithms is highly dependent on the initial solutions, commonly obtained by running a sampling-based planner to obtain a collision-free path. However, these methods can be slow in high-dimensional and complex scenes and produce nonsmooth solutions. Given previously solved path-planning problems, it is highly desirable to learn their distribution and use it as a prior for new similar problems. Several works propose utilizing this prior to bootstrap the motion planning problem, either by sampling initial solutions from it, or using its distribution in a maximum-a-posterior formulation for trajectory optimization. In this work, we introduce motion planning diffusion (MPD), an algorithm that learns trajectory distribution priors with diffusion models. These generative models have shown increasing success in encoding multimodal data and have desirable properties for gradient-based motion planning, such as cost guidance. Given a motion planning problem, we construct a cost function and sample from the posterior distribution using the learned prior combined with the cost function gradients during the denoising process. Instead of learning the prior on all trajectory waypoints, we propose learning a lower dimensional representation of a trajectory using linear motion primitives, particularly B-spline curves. This parametrization guarantees that the generated trajectory is smooth, can be interpolated at higher frequencies, and needs fewer parameters than a dense waypoint representation. We demonstrate the results of our method ranging from simple 2-D to more complex tasks using a 7-DOF robot arm manipulator. In addition to learning from simulated data, we also use human demonstrations on a real-world pick-and-place task. The experiment results show that diffusion models are strong priors for encoding multimodal trajectory distributions for optimization-based motion planning.
Toward Controllable and Trustworthy LLM Reasoning: From Failure Mapping to Cognition-inspired Control and Real-world Impact
PDF ↗Large Language Models (LLMs) have advanced rapidly and raised the bar for what AI is expected to do. However, accompanied with such progress is a stronger consensus that these models consistently fail in out-of-distribution reasoning, especially on tasks that require abstraction, transfer, or long-horizon planning. While acceptable for most consumer use, these issues prevent AI from being safely deployed in high-stakes settings (e.g., healthcare), where stakeholders cannot trust AI models that exhibit uncontrollable and unpredictable failures. In this talk, I will discuss our work and insights on how to make LLM reasoning controllable and trustworthy, by 1) understanding the mechanisms of LLM reasoning and predicting when LLM will fail; 2) improving model reasoning and generalization based on such insights; and 3) moving towards trustworthy AI applications through such improvements, and identifying new problems to form a healthy positive-feedback loop.
Data-Efficient and Contact-Rich Manipulation Through Diffusion Augmentation and Vision-Language Models
PDF ↗Recent progress in robot learning has produced impressive results, yet many systems still require learning from large datasets of demonstrations and are less effective in clutter or with highly deformable objects. This talk presents work on data-efficient manipulation using (i) diffusion-based augmentation that synthesizes geometrically consistent images and action labels to reduce demonstration requirements and (ii) Vision-Language Models (VLMs) that inject high-level semantics for contact-rich motion planning in clutter. We will also introduce ManipBench, which evaluates VLMs’ abilities for low-level manipulation. Together, we show how to move the community towards achieving robot manipulators that can learn and operate with reduced demonstration requirements across cluttered and real-world environments.
Unlocking the Power of Large Multimodal Models for Robot Learning: Robustness, Generalization, and Opportunities
PDF ↗Large multimodal models (LMMs) have revolutionized AI by demonstrating remarkable capabilities in vision, language, audio, and other domains, particularly in understanding and generalization tasks. Yet, moving beyond passive understanding to active interaction requires embodied agents, such as robots, that can harness the capabilities of AI models to act within the physical world. My core research aims to build embodied agents that reason about and interact with the physical world with human-like commonsense. Specifically, I design algorithms and representations that enable robots to perceive their environment, reason about physical properties, and plan long-horizon actions for both manipulation and locomotion. These advances are grounded in the integration of large-scale AI models with embodied control. I organize this agenda into three stages: (1) injecting actions into LMMs to form vision–language–action (VLA) models; (2) learning from human motion and contact to enrich physical reasoning; and (3) advancing whole-body robot loco-manipulation guided by LMMs toward embodied artificial general intelligence (AGI). The talk details recent advances in leveraging LMMs for robot learning, emphasizing the promise of robust generalization across diverse environments, tasks, and modalities. I will highlight contributions at the intersection of perception, reasoning, and control, and outline open challenges and future opportunities toward enabling humanoid robots that can robustly understand, interact, and collaborate with humans in complex real-world settings.
Current mainstream AI, at least as presented in media and measured by the number of people involved and papers published, is mainly about big data, deep learning, and recently trendy large language models. All these are techniques that are data-driven, model-free, and number-crunching. Their immense success in some areas, such as computer vision and natural language processing, started the next hype in the era of AI, which brings a question whether neural approaches, after being dismissed at the beginning of the AI era, finally conquered the world of AI and proved applicable to every problem. A deeper look at these new techniques shows they have similar issues as the old-fashioned AI techniques in the past: brittleness, making strange mistakes, and being highly dependent on data used for training. Moreover, there are problems with the explainability of results and no guarantees provided, which is a crucial issue in some application areas. In this paper, we look at core principles of the neural ML techniques, that is, being data-driven rather than knowledge-based and being model-free rather than model-based, and we argue that symbolic knowledge models can still contribute to the design of trustworthy and explainable AI systems. Specifically, we focus on hierarchical reasoning, namely hierarchical planning, which is useful for highly complex problems but is not addressed by current neural models. We propose a research plan consisting of solving specific problems in hierarchical planning as an example of a knowledge-intensive approach to problem-solving. We show close connections between these problems that allow a smooth transition between solving techniques used to solve these problems. We also propose an ultimate goal of this endeavor, that is, autonomous construction of hierarchical planning models, that addresses the crucial problem of knowledge-based approaches -- how to obtain a formal model (extract knowledge from data).
Early warning systems for disease outbreaks play a crucial role in public health for management and contingency planning. However, most predictive modeling works focus on flat models that incorporate exogenous inputs (e.g. climate, demographics) to predict future outbreaks at different locations, but do not jointly model multiple spatial aggregation levels. In this paper, we introduce HierarNet, a unique independent-interactive hierarchical forecasting framework that aims to predict disease outbreaks at different levels of spatial resolution, such as provinces, regions, and nations. HierarNet consists of two main phases. In the local phase, we train independent forecasting models for all locations at all levels. In the global phase, all models iteratively interact with others across different levels via their hierarchical relationships under an ensemble fashion to maximize their agreements. This global local hierarchical interactive scheme makes HierarNet a highly effective and flexible method (i.e. it can work with an arbitrary base prediction model and available exogenous data for each location independently). Extensive experiments are conducted on various disease datasets (e.g., Dengue fever, flu, diarrhea, and Bluetongue) in different countries (e.g., France, Vietnam, and USA) to show the performance of HierarNet compared to 19 state-of-the-art (SOTA) methods such as MinT, DYCHEM, WITRAN, SegRNN, TSMixer, PatchTST, or iTransformer. We also illustrate the generability of HierarNet in other domains, e.g., web traffic forecasting.
Energy usage prediction is important for various real-world applications, including grid management, infrastructure planning, and disaster response. Although a plethora of deep learning approaches have been proposed to perform this task, most of them either overlook the essential spatial correlations across households or fail to scale to individualized prediction, making them less effective for accurate fine-grained user-level prediction. In addition, due to the dynamic and uncertain nature of energy usage caused by various factors such as extreme weather events, quantifying uncertainty for reliable prediction is also significant, but it has not been fully explored in existing work. In this paper, we propose a unified framework called TrustEnergy for accurate and reliable user-level energy usage prediction. There are two key technical components in TrustEnergy, (i) a Hierarchical Spatiotemporal Representation module to efficiently capture both macro and micro energy usage patterns with a novel memory-augmented spatiotemporal graph neural network, and (ii) an innovative Sequential Conformalized Quantile Regression module to dynamically adjust uncertainty bounds to ensure valid prediction intervals over time, without making strong assumptions about the underlying data distribution. We implement and evaluate our TrustEnergy framework by working with an electricity provider in Florida, and the results show our TrustEnergy can achieve a 5.4% increase in prediction accuracy and 5.7% improvement in uncertainty quantification compared to state-of-the-art baselines.
Understanding group-level social interactions in public spaces is crucial for urban planning, informing the design of socially vibrant and inclusive environments. Detecting such interactions from images involves interpreting subtle visual cues such as relations, proximity and co-movement – semantically complex signals that go beyond traditional object detection. To address this challenge, we introduce a social group region detection task, which requires inferring and spatially grounding visual regions defined by abstract interpersonal relations. We propose MINGLE (Modeling INterpersonal Group-Level Engagement), a modular three-stage pipeline that integrates: (1) off-the-shelf human detection and depth estimation, (2) VLM-based reasoning to classify pairwise social affiliation, and (3) a lightweight spatial aggregation algorithm to localize socially connected groups. To support this task and encourage future research, we present a new dataset of 100K urban street-view images annotated with bounding boxes and labels for both individuals and socially interacting groups. The annotations combine human-created labels and outputs from the MINGLE pipeline, ensuring semantic richness and broad coverage of real world scenarios.
LSDTs: LLM-Augmented Semantic Digital Twins for Adaptive Knowledge-Intensive Infrastructure Planning
PDF ↗Digital Twins (DTs) offer powerful tools for managing complex infrastructure systems, but their effectiveness is often limited by challenges in integrating unstructured knowledge. Recent advances in Large Language Models (LLMs) bring new potential to address this gap, with strong abilities in extracting and organizing diverse textual information. We therefore propose LSDTs (LLM-Augmented Semantic Digital Twins), a framework that helps LLMs extract planning knowledge from unstructured documents like environmental regulations and technical guidelines, and organize it into a formal ontology. This ontology forms a semantic layer that powers a digital twin—a virtual model of the physical system—allowing it to simulate realistic, regulation-aware planning scenarios. We evaluate LSDTs through a case study of offshore wind farm planning in Maryland, including its application during Hurricane Sandy. Results demonstrate that LSDTs support interpretable, regulation-aware layout optimization, enable high-fidelity simulation, and enhance adaptability in infrastructure planning. This work shows the potential of combining generative AI with digital twins to support complex, knowledge-driven planning tasks.
Agentmandering: A Game-Theoretic Framework for Fair Redistricting via Large Language Model Agents
PDF ↗Redistricting plays a central role in shaping how votes are translated into political power. While existing computational methods primarily aim to generate large ensembles of legally valid districting plans, they often neglect the strategic dynamics involved in the selection process. This oversight creates opportunities for partisan actors to cherry-pick maps that, while technically compliant, are politically advantageous. Simply satisfying formal constraints does not ensure fairness when the selection process itself can be manipulated. We propose Agentmandering, a framework that reimagines redistricting as a turn-based negotiation between two agents representing opposing political interests. Drawing inspiration from game-theoretic ideas, particularly the Choose-and-Freeze protocol, our method embeds strategic interaction into the redistricting process via large language model (LLM) agents. Agents alternate between selecting and freezing districts from a small set of candidate maps, gradually partitioning the state through constrained and interpretable choices. Evaluation on post-2020 U.S. Census data across all states shows that Agentmandering significantly reduces partisan bias and unfairness, while achieving 2 to 3 orders of magnitude lower variance than standard baselines. These results demonstrate both fairness and stability, especially in swing-state scenarios.
Satellite-based slum segmentation holds significant promise in generating global estimates of urban poverty. However, the morphological heterogeneity of informal settlements presents a major challenge, hindering the ability of models trained on specific regions to generalize effectively to unseen locations. To address this, we introduce a large-scale high-resolution dataset and propose GRAM (Generalized Region-Aware Mixture-of-Experts), a two-phase test-time adaptation framework that enables robust slum segmentation without requiring labeled data from target regions. We compile a million-scale satellite imagery dataset from 12 cities across four continents for source training. Using this dataset, the model employs a Mixture-of-Experts architecture to capture region-specific slum characteristics while learning universal features through a shared backbone. During adaptation, prediction consistency across experts filters out unreliable pseudo-labels, allowing the model to generalize effectively to previously unseen regions. GRAM outperforms state-of-the-art baselines in low-resource settings such as African cities, offering a scalable and label-efficient solution for global slum mapping and data-driven urban planning.
The core of efficient on-demand ride-pooling lies in solving the Ride-Pool Matching Problem (RMP), which involves assigning multiple customer requests to single vehicles under various service constraints (e.g., pickup windows, detour allowances, and vehicle occupancy). A significant missed opportunity in most current RMP approaches is the assumption that passengers must be picked up and dropped off exactly at their requested locations. Allowing passengers to walk even short distances to meet vehicles could unlock substantial improvements in ride-pooling operations. Building upon the limitations of existing Ride-Pool Matching Problem (RMP) solutions that neglect passenger walkability, this paper introduces a novel matching method that strategically incorporates flexible pickup and drop-off locations. Our approach simultaneously determines the optimal assignment of vehicles to requests (one vehicle to potentially multiple requests and each request to at most one vehicle), identifies advantageous meeting points for passengers, and plans efficient vehicle routes. This comprehensive optimization respects all service constraints and considers the long-term implications of routing decisions. To achieve this integrated solution, we first employ a tree-based approach to enumerate all feasible pairings between passengers and vehicles. Subsequently, we calculate an optimal route for each of these feasible matches. Finally, we evaluate the quality of all possible assignments and select the most advantageous matching for implementation. In our experimental evaluation on city-scale taxi datasets, we demonstrate that our method improves the number of served requests by up to 13% and reduces the average vehicle travel distance by up to 21%. By serving more passengers with less driving distance, our approach achieves greater efficiency in a more sustainable manner — using fewer resources to deliver better service and creating a win-win outcome for all stakeholders, including customers, drivers, the aggregator, and the environment.
Creating a well-structured lesson plan is essential for improving classroom efficiency, yet it is often a labor-intensive process. Recently, many studies have leveraged large language models (LLMs) to generate lesson plans automatically. However, existing methods heavily rely on LLMs that are pre-trained on large-scale universal corpora, which often lack critical educational theory and textbook-specific information. This can lead to inconsistencies and misalignments with textbook content. To address these challenges, we propose CE-LessonPlan, a novel compress-expand framework to generate lesson plans by effectively combining external lesson plan references and textbook information. The framework consists of two key components: a compressor, which synthesizes multiple retrieved references into a cohesive document, and an expander, which integrates textbook-specific information with the parametric knowledge of LLMs to produce another enriched lesson plan. The outputs of the compressor and expander are then seamlessly integrated to create a comprehensive golden context, further enhancing the lesson plan generation process with LLMs. We conduct extensive experiments to demonstrate that CE-LessonPlan outperforms existing methods for generating lesson plans.
EIDSeg: A Pixel-Level Semantic Segmentation Dataset for Post-Earthquake Damage Assessment from Social Media Images
PDF ↗Rapid post‑earthquake damage assessment is crucial for rescue and resource planning. Still, existing remote sensing methods depend on costly aerial images, expert labeling, and produce only binary damage maps for early-stage evaluation. Although ground-level images from social networks provide a valuable source to fill this gap, a large pixel-level annotated dataset for this task is still unavailable. We introduce EIDSeg, the first large-scale semantic segmentation dataset specifically for post-earthquake social media imagery. The dataset comprises 3,266 images from nine major earthquakes (2008–2023), annotated across five classes of infrastructure damage. Undamaged Building, Damaged Building, Destroyed Building, Undamaged Road, and Damaged Road. We propose a practical three-phase cross-disciplinary annotation protocol with labeling guidelines that enables consistent segmentation by non-expert annotators, achieving over 70% inter-annotator agreement. We benchmark several state-of-the-art segmentation models, identifying Encoder-only Mask Transformer (EoMT) as the top-performing method with a Mean Intersection over Union (mIoU) of 80.8%. By unlocking social networks' rich, ground-level perspective, our work paves the way for a faster, finer-grained damage assessment in the post-earthquake scenario.
Assistive robotics is an important subarea of robotics that focuses on the well-being of people with disabilities. A robotic guide dog is an assistive quadruped robot for assisting visually impaired people in obstacle avoidance and navigation. Enabling language capabilities on robotic guide dogs goes beyond naively adding an existing dialog system onto a mobile robot. The novel challenges include grounding language to the dynamically changing environment and improving spatial awareness for the human handler. To address those challenges, we develop a novel dialog system for robotic guide dogs that uses large language models to verbalize both navigational plans and scenes. The goal is to enable verbal communication for collaborative decision-making within the handler-robot team. In experiments, we performed a human study to evaluate different verbalization strategies, and a simulation study to evaluate the efficiency and accuracy in navigation tasks.
Global biodiversity loss is accelerating, prompting international efforts such as the Kunming-Montreal Global Biodiversity Framework (GBF) and the United Nations Sustainable Development Goals to direct resources toward halting species declines. A key challenge in achieving this goal is having access to robust methodologies to understand where species occur and how they relate to each other within broader ecological communities. Recent deep learning-based advances in joint species distribution modeling have shown improved predictive performance, but effectively incorporating community-level learning, taking into account species-species relationships in addition to species-environment relationships, remains an outstanding challenge. We introduce LabelKAN, a novel framework based on Kolmogorov-Arnold Networks (KANs) to learn inter-label connections from predictions of each label. When modeling avian species distributions, LabelKAN achieves substantial gains in predictive performance across the vast majority of species. In particular, our method demonstrates strong improvements for rare and difficult-to-predict species, which are often the most important when setting biodiversity targets under frameworks like GBF. These performance gains also translate to more confident predictions of the species spatial patterns as well as more confident predictions of community structure. We illustrate how the LabelKAN leads to qualitative and quantitative improvements with a focused application on the Great Blue Heron, an emblematic species in freshwater ecosystems that has experienced significant population declines across the United States in recent years. Using the LabelKAN framework, we are able to identify communities and species in New York that will be most sensitive to further declines in Great Blue Heron populations. Our results underscore the critical importance of incorporating information on community assemblage in species distribution modeling. By leveraging species co-occurrence patterns, our approach offers deeper ecological insights and supports more informed conservation planning in the face of accelerating biodiversity loss. Beyond species distribution modeling, LabelKAN provides a principled approach to capturing inter-label connections and can generalize to diverse multi-label tasks. We hope it encourages further research on inter-label learning across domains.
Optimizing Health Coverage in Ethiopia: A Learning-augmented Approach and Persistent Proportionality Under an Online Budget
PDF ↗As part of nationwide efforts aligned with the United Nations' Sustainable Development Goal 3 on Universal Health Coverage, Ethiopia's Ministry of Health is strengthening health posts to expand access to essential healthcare services. However, only a fraction of this health system strengthening effort can be implemented each year due to limited budgets and other competing priorities, thus the need for an optimization framework to guide prioritization across the regions of Ethiopia. In this paper, we develop a tool, Health Access Resource Planner (HARP), based on a principled decision-support optimization framework for sequential facility planning that aims to maximize population coverage under budget uncertainty while satisfying region-specific proportionality targets at every time step. We then propose two algorithms: (i) a learning-augmented approach that improves upon expert recommendations at any single-step; and (ii) a greedy algorithm for multi-step planning, both with strong worst-case approximation estimation. In collaboration with the Ethiopian Public Health Institute and Ministry of Health, we demonstrated the empirical efficacy of our method on three regions across various planning scenarios.