This paper addresses the Motion Execution Gap, the disconnect between high-level symbolic task descriptions using semantic constraints and executable robot motions. \textbf{Motion Statecharts} are introduced as an executable symbolic representation for complex motions. They allow the arbitrary arrangement of motion constraints, monitors or nested statecharts in parallel and sequence. World-centric motion specification and generalization across embodiments are enabled through the use of a unified differentiable kinematic world model of both, robots and environments. Motion execution is realized through a \ac{lmpc}-based implementation of the task-function approach, in which smooth transitions during task switches are ensured using jerk bounds. Cross-platform transferability was demonstrated by deploying the method on eight robot platforms, operating in diverse environments. The proposed framework is called Giskard and is available open source (https://github.com/cram2/cognitive_robot_abstract_machine).
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The increasing sensitivity to human aspects in autonomous systems engineering calls for principled approaches to embed ethical and normative concerns into their behaviour. Indeed, recent research has focused on expressing and validating sets of social, legal, ethical, empathetic, and cultural (SLEEC) concerns as rules. However, existing work is limited to rule specification or verification, leaving the problem of semantic-preserving operationalisation of ethical rules in autonomous systems largely unaddressed. For this purpose, we provide an operational solution for ethical-aware autonomous systems, applied to the realm of multi-service robots. Specifically, we devise a principled approach, named CO-SLEEC (Context-Oriented SLEEC), connecting the normative setting of SLEEC rules to context-oriented programming (COP). CO-SLEEC enables runtime adaptation while preserving the semantics of SLEEC rules during robot task execution. It features two reusable Python libraries for (i) parsing SLEEC rules into contextual elements for operationalising them, and (ii) connecting the operational model to the Robot Operating System (ROS), respectively. We evaluate our implementation for correctness, efficiency, and maintainability over multi-service assistive robot scenarios.
In the final "Last-Centimeter" phase of manipulation, where visual occlusion or calibration errors render vision unreliable, robots often suffer high failure rates due to local pose uncertainty and simulation dynamics deviations. To address these issues, this paper proposes the PECHC (Physics-Evolving Cascade Constraint and Human-Correction) algorithm. To rigorously isolate the contribution of tactile feedback in multi-finger coordination, we adopt a decoupled control strategy that focuses on grasp stabilization within the hand's workspace, acting as a fail-safe reflex. The core of our approach is Hybrid Correction Imitation Learning (HCIL), which establishes a "failure-triggered" human-machine mechanism to efficiently resolve the "model gap" via sparse expert corrections. To ensure sample efficiency and baseline performance, we introduce two supporting modules: Cascaded Constraint Scheduling (CCS) addresses the "geometric gap" by enforcing physically plausible behavioral constraints (geometric approach, force closure, and dynamic stability), while Temporal Heterogeneous Distillation (THED) resolves the "physical gap" by enabling implicit system identification from tactile history. Experiments demonstrate that PECHC achieves a 97.3% real-robot success rate on 150 objects from the Visual Dexterity Dataset under fully autonomous testing, where one object is used for one-time HCIL calibration and the remaining 149 objects are evaluated without further intervention. Compared to a standard Sim-to-Real reinforcement learning baseline (Vanilla PPO with Domain Randomization), PECHC delivers a significant performance improvement (+42.8%) and exhibits human-like force modulation capabilities for fragile objects.
Robots that follow natural-language instructions often either plan at a high level using hand-designed interfaces or rely on large end-to-end models that are difficult to deploy for real-time control. We propose TeNet (Text-to-Network), a framework for instantiating compact, task-specific robot policies directly from natural language descriptions. TeNet conditions a hypernetwork on text embeddings produced by a pretrained large language model (LLM) to generate a fully executable policy, which then operates solely on low-dimensional state inputs at high control frequencies. By using the language only once at the policy instantiation time, TeNet inherits the general knowledge and paraphrasing robustness of pretrained LLMs while remaining lightweight and efficient at execution time. To improve generalization, we optionally ground language in behavior during training by aligning text embeddings with demonstrated actions, while requiring no demonstrations at inference time. Experiments on MuJoCo and Meta-World benchmarks show that TeNet produces policies that are orders of magnitude smaller than sequence-based baselines, while achieving strong performance in both multi-task and meta-learning settings and supporting high-frequency control. These results show that text-conditioned hypernetworks offer a practical way to build compact, language-driven controllers for ressource-constrained robot control tasks with real-time requirements.
Vision-language-action models (VLAs) often achieve high performance on demonstrated tasks but struggle significantly when required to extrapolate, recombining skills used in different tasks in novel ways. For instance, VLAs might successfully put the cream cheese in the bowl and put the bowl on top of the cabinet, yet still fail to put the cream cheese on top of the cabinet. This motivates us to investigate whether VLAs merely overfit to demonstrated tasks or still hold the potential to extrapolate. Our study uses text latent as the ingredient; it is a task-specific vector derived from the models’ hidden states. It thus encodes semantics necessary for completing a task and can be used to reconstruct the associated task behavior by writing it to the model’s residual stream. Furthermore, we find that skills used in distinct tasks can be combined to produce novel behaviors by blending their respective text latent. Applying this to π0, we increase its success rate from 9% to 83% on the proposed libero-ood benchmark, which features 20 tasks extrapolated from standard LIBERO tasks. This reveals that the skill representations encoded in text-latent are individual yet composable, while π0 fails to autonomously combine these representations for extrapolation. This also validates the design of libero-ood; it comprises tasks that the model fails, yet should be able to complete. We then tested other VLAs on libero-ood, and none of them achieved a success rate higher than 21%. Further analysis reveals VLAs share a common pattern to exhibit spatial overfitting, associating object names with where the object is spatially located in the demonstrated scene rather than achieving true object and goal understanding.
Robotic perception in unstructured environments remains challenging despite the zero-shot capabilities of foundation models such as SAM. This work attributes performance degradation to non-uniform representation shifts across transformer layers: shallow layers exhibit substantial domain gaps (CKA < 0.5), whereas deep layers transfer effectively (CKA > 0.7). Based on this observation, we propose RepSAM, a representation-guided parameter-efficient fine-tuning (PEFT) framework for adapting foundation models to robotic vision. RepSAM employs a theoretically grounded CKA-guided rank allocation strategy combined with a multi-modal fusion module for robust handling of challenging robotic scenarios, including transparent objects and cluttered scenes. Experimental evaluation across six benchmarks and robotic manipulation tasks demonstrates that RepSAM achieves 97.9% of full fine-tuning performance (89.0% vs. 90.9% mIoU) while reducing trainable parameters by 158× (from 632M to 4.0M). RepSAM outperforms DoRA by 7.9% mIoU with just 4 hours of training on a single A100 GPU (a 96× reduction from full fine-tuning, which takes 384 GPU-hours). These improvements are statistically significant (p<0.01) and translate to a 12.0% absolute improvement in robotic manipulation success rates over the LoRA (RGB) baseline.
Safe and precise maneuvering of quadrotor unmanned aerial vehicles (UAVs) in high-speed wind environments remains a critical challenge. Wind disturbances are nonlinear, time-varying, and difficult to model, causing traditional controllers to struggle with perception and compensation, especially under unseen wind distributions. To address these limitations, we introduce WA-TD3, a data-driven control framework that enables real-time wind disturbance perception and adaptive compensation without dedicated wind sensors. WA-TD3 employs a deep residual network to extract wind characteristics from temporal patterns in state deviations, forming a dynamics residual-driven perception mechanism that implicitly models and compensates for unknown winds. This residual is integrated into a perception-augmented reinforcement learning architecture, providing the policy with enhanced state information for proactive disturbance-aware control. Extensive experiments on complex trajectories under varying wind intensities demonstrate that WA-TD3 consistently outperforms state-of-the-art methods, achieving over 62% improvement in tracking accuracy under strong winds.
The robustness of autonomous vehicles such as drones and Unmanned Surface Vehicles (USV) is crucial when facing unknown and complex marine environments, especially when heteroscedastic observational noise poses significant challenges to sensor-based navigation tasks. Recently, Distributional Reinforcement Learning (DistRL) has shown promising results in some challenging autonomous navigation tasks without prior environmental information. However, these methods overlook situations where noise patterns vary across different environmental conditions, hindering safe navigation and disrupting the learning of value functions. To address the problem, we propose DRIQN to integrate Distributionally Robust Optimization (DRO) with implicit quantile networks to optimize worst-case performance under natural environmental conditions. Leveraging explicit subgroup modeling in the replay buffer, DRIQN incorporates heterogeneous noise sources and target robustness-critical scenarios. Experimental results based on the risk-sensitive environment demonstrate that DRIQN significantly outperforms state-of-the-art meth- ods, achieving +13.51% success rate, -12.28% collision rate and +35.46% for time saving, +27.99% for energy saving, compared with the runner-up.
Vision-and-language navigation (VLN) requires agents to follow natural language instructions to navigate autonomously in continuous environments. However, existing approaches often lack high-level semantic guidance in waypoint prediction and explicit language–landmark alignment in cross-modal planning. To address these limitations, we propose FILD-Nav, a vision-and-language navigation framework that integrates instruction landmark features. FILD-Nav extracts task-relevant landmarks from instructions and incorporates landmark semantics into both waypoint prediction and topological planning. Specifically, landmark-guided waypoint prediction improves waypoint relevance, while landmark-enhanced cross-modal planning enables more effective long-horizon navigation. Extensive experiments on the VLN-CE benchmark demonstrate that FILD-Nav consistently outperforms prior methods, achieving improvements of 2% in Success Rate (SR), 3% in Success weighted by Path Length (SPL), and 7% in Oracle Success Rate (OSR), particularly in unseen environments.
Offline robotic control requires long-horizon reasoning from fixed datasets while avoiding unsafe extrapolation beyond demonstrated behavior. We propose GRALP, a principled framework that resolves this tension by jointly enforcing support preservation and controllability at the level of temporal abstraction. GRALP adopts a deliberate architectural separation: diffusion is used exclusively as a deterministic action decoder for executing fixed latent skills, while planning and value estimation operate entirely in latent space under conservative constraints. This design enables stable value learning, controllable skill composition, and efficient planning without trajectory-level diffusion sampling at inference. Across unified D4RL benchmarks, GRALP achieves the highest average performance on Navigation, Sequential (Kitchen), and Adroit domains while remaining competitive on locomotion tasks. On contact-rich RoboSuite manipulation with human demonstrations (Lift and Pick-and-Place), GRALP achieves consistently high success rates (over 94%). These results indicate that reliable long-horizon offline control emerges when expressivity is confined to execution and decision-making operates over support-aligned latent abstractions.
Foundation Models for Electronic Health Records (FEMRs) are pretrained on large-scale structured patient data, enabling them to convert longitudinal patient trajectories into generalizable representations for diverse clinical prediction tasks. Despite their effectiveness, FEMRs remain black-box models, raising concerns about bias, interpretability, and clinical trust. To address this, we propose the first token-level explainability approach for FEMRs. We train a Transformer-based surrogate model on input-output pairs from the FEMR across two prediction tasks, approximating its behavior while preserving temporal dynamics. We identify the most influential tokens, providing insights into how FEMRs leverage different aspects of patient history for predictions. To evaluate clinical relevance, we introduce a novel clinical alignment metric that quantifies the correspondence between the surrogate model’s key tokens and clinically validated features. Our results demonstrate that the surrogate closely approximates FEMR predictions and that token-level explanations align well with clinical knowledge, offering a practical framework for interpretable and trustworthy clinical AI.
Multimodal medical imaging benefits from the global context modeling of transformers, yet most existing models fuse modalities implicitly by channel concatenation, leaving cross-modal interaction unstructured or relying on costly multi-stream cross-attention. We propose Modality-Aware Token Interaction (MATI), an architecturally lightweight and backbone-agnostic module that structures multimodal interaction within a single token stream by partitioning embedding channels into modality-aligned subspaces. MATI performs modality-preserving intra-subspace self-attention and gated global mixing for controlled inter-subspace exchange. We instantiate MATI in UNETR and introduce two architectures: ModaUNETR^S refines selected skip-token representations, and ModaUNETR^E injects MATI into the transformer encoder to progressively shape the token hierarchy. Experiments on the BraTS 2020 benchmark employ five-fold cross-validation and report segmentation and efficiency metrics on the official training and validation sets. Both models consistently improve over UNETR across all tumor subregions, with larger gains for tumor core and whole tumor, and ModaUNETR^E further improves upon ModaUNETR^S. An ablation study confirms monotonic gains from structured interaction. Compared with heavier transformers, the proposed models achieve a favorable accuracy-efficiency trade-off without modality-specific encoders or quadratic cross-attention, supporting structured multimodal interaction as a first-class architectural principle. Implementation of MATI and its instantiations is available at https://github.com/S3l11/MATI.
In gene expression analysis, high dimension low sample size data limits the applicability of deep learning, motivating increasing interest in variable-length evolutionary algorithm–based feature selection methods (VLEAs). However, existing VLEAs suffer from unreliable single-metric discrimination under dynamic search-space variation, mismatches between population size and search space dimensionality, and particle performance degradation after search space changes. To this end, a Multiscale-adaptive and Size-adaptive Particle Swarm Optimization (MASA-PSO) is proposed for gene expression analysis. MASA-PSO adopts a multiscale-adaptive weighting framework to explore feature subsets that distinguish between-class sample distributions during search spaces changes, and theoretically proves it enables the collaborative evaluation of multiple metrics. Meanwhile, it proposes an adaptive population division that explicitly models the functional relationship between the population size and the search space to resolve the mismatch. Furthermore, a particle degradation phenomenon impairing the performance of VLEAs is observed and alleviated through a hybrid elite strategy. Experiments on ten gene expression datasets verify that MASA-PSO outperforms state-of-the-art methods in classification accuracy while capturing smaller feature subsets.
While Large Language Models (LLMs) offer promise in scientific discovery, leveraging LLMs to drive biomedical research requires the scientific discovery process to be performed in combination with cutting-edge biomedical research and rigorous mechanistic causal chains. As such, both current Retrieval-augmented generation methods lacking causal reasoning capabilities, and the static traditional knowledge graphs failing to reflect evolving scientific knowledge, present obstacles to utilizing LLMs as scientific discovery tools. In response to these ongoing challenges, we present THGAgents. THGAgents utilize collaborative and dynamically updating agents to build a Traceable Causal Knowledge Graph, which serves as the foundation for the evidence-based knowledge structure. Crucially, we employ an LLM-driven heuristic search algorithm to traverse the complex network, balancing both novelty and rigor to deduce strict, evidence-based mechanistic causal chains. Additionally, THGAgents utilize a generator-critic loop to support hypothesis refinement. In experimental benchmarks across both cancer systems and neuroscience, THGAgents achieved up to a 0.80 hit rate in predicting validated scientific discoveries, providing an almost 9.5% increase in hypothesis quality scores versus current state-of-the-art systems, and decreasing the mechanistic hallucination rate to 1.12%. Our code is available at https://github.com/yangCode-res/THGAgents/.
Deploying diabetic retinopathy (DR) screening models in primary care requires edge-efficient systems that remain accurate, safe, and reliable under domain shift. Multi-teacher knowledge distillation (KD) is a natural compression strategy, but existing approaches largely assume that all teachers provide equally trustworthy supervision. In our setting, this assumption fails: a strong CNN teacher (EfficientNet-B3, 0.876 QWK) and a weaker Transformer teacher (Swin-Base, 0.830 QWK) are complementary, yet the Transformer's logits can still mislead the student. We therefore propose OrthKD, a selective-trust distillation framework that transfers full supervision from the strong CNN, uses feature-only distillation from the weak ViT, and enforces orthogonality between teacher-specific student projections to encourage complementary rather than redundant evidence. This design preserves local lesion precision, injects global structural context, and improves robustness to distribution shift. On 132,049 retinal images, a 5.4M-parameter MobileNetV3 student reaches 0.885 QWK on EyePACS and improves zero-shot Messidor-2 performance from 0.507 to 0.728 QWK, while also achieving strong referral AUC and calibration. These results show that selectively distilling heterogeneous teachers can enable practical DR screening on resource-constrained devices.
Temporal constraints are an intrinsic component of most clinical guidelines. Several approaches to computerized clinical guidelines (CIGs) offer temporal reasoning facilities to support the execution of a CIG for a specific patient, mostly based on bounds on differences and on the Simple Temporal Problem framework. However, in scheduling activities (e.g., within a hospital), it is necessary to consider multiple executions of CIGs for different patients, which can also be (partly) related to each other. In this work we extend current temporal reasoning techniques to apply to such a context. We propose a new temporal constraint model, prove its properties, and exploit them to provide efficient management of patients' temporal constraints, and to support efficient query answering. We also propose an experimental evaluation, demonstrating the step forward with respect to current approaches. Notably, our approach is general, and can apply to all temporal reasoning problems having the same topology of the "multiple CIG execution" problem.
Activated PI3Kδ Syndrome (APDS) is a rare genetic immune disorder caused by variants in PIK3CD or PIK3R1, with highly heterogeneous symptoms that often delay diagnosis. Early recognition is hampered by overlapping clinical presentations and limited clinician awareness, motivating systematic, data-driven approaches to detect APDS-associated phenotypic patterns in routine electronic health records. Traditional linear scoring systems cannot capture complex symptom interactions, while deep learning models, though expressive, often lack interpretability. To bridge this gap, we propose Shapley regression, a novel game-theoretic model replacing the linear predictor with a k-additive cooperative game, explicitly modeling co-occurrence of symptoms while maintaining the transparency and convexity of logistic regression. We carry out an empirical study of our lightweight method on eight public biomedical datasets, showing that a 2-additive model with ℓ2 regularization achieves an optimal trade-off between predictive power and noise robustness. We apply it to a real-world cohort of 222 patients, on which Shapley regression accurately distinguished APDS cases from matched controls, confirming and validating phenotypes known to be associated with APDS, and facilitating the exploration of pairwise interactions between symptoms, validated by clinical experts.
Recent breakthroughs in Vision-Language Models (VLMs) have shown strong potential in medical analysis, but they remain limited in the brain lesion image domain, especially when pathological regions occupy a small portion of the image. This is because VLMs tend to put excessive attention on background regions that are visually similar to target regions. Based on the findings, we propose SAM-GPT, a novel framework that leverages segmentation-derived spatial priors to support VLM-based lesion classification. It first uses an enhanced segmentation model to localize pathological regions for diagnostic tasks, and then converts lesion attributes (e.g., size, pixel range, lesion location) into linguistic guidance for a vision–language model. To improve small lesion recognition, we incorporate a new Hilbert scanning method into Mamba that improves both local spatial continuity and global spatial modeling. Experiments on benchmark datasets show that our model achieves an average Dice coefficient of 72.80% on the brain lesion segmentation task and an accuracy of 80.56% on the brain disease classification task, demonstrating the effectiveness of the proposed framework. The code is available at https://github.com/1JF-Wang/sam-gpt.
Federated Learning with LoRA fine-tuning offers an efficient and privacy-aware solution for institutions to collaboratively leverage their large datasets to train VLLMs. However, participating institutions often possess heterogeneous computational resources, resulting in imbalanced LoRA ranks, which pose a major challenge for effective collaboration. In addition, real-world applications in domains such as healthcare and transportation frequently suffer from missing modalities due to user mistakes or device failures, which significantly degrade global model performance in federated settings. To the best of our knowledge, no prior work has addressed these two challenges simultaneously in federated VLLMs. To tackle these issues, we propose FediLoRA, a lightweight federated LoRA aggregation framework that effectively mitigates the impact of missing modalities in heterogeneous environment. FediLoRA is explicitly motivated by the observation that simple averaging and structured editing can jointly benefit both global and personalized models. Our approach achieves strong performance across multiple general-domain and medical-domain benchmark datasets. Additional experiments on healthcare data further demonstrate that FediLoRA is well-suited for practical, real-world deployment scenarios. Our code is released at https://github.com/gotobcn8/FediLoRA.
Contrastive vision–language pretraining models such as CLIP align images and text in a shared embedding space but do not explicitly model or evaluate the hierarchical semantics common in medical image interpretation. We propose HCE-CLIP (Hierarchical Conditional Energy CLIP), a vision–language pretraining framework that formulates medical image–text alignment as a hierarchical label-conditional energy modeling problem. HCE-CLIP encodes an image series using transformer-based aggregation and aligns it with free-text reports and structured label state prompts across multiple semantic levels. At each level, conditional energy functions favor clinically consistent label states while suppressing contradictory alternatives, enabling uncertainty-aware inference. To assess semantic coherence, we introduce a hierarchical contradiction-based metric that quantifies logical inconsistencies between fine-grained disease predictions and higher-level clinical summaries. Experiments on MIMIC-CXR and other public benchmarks show that HCE-CLIP outperforms existing medical vision–language pretraining methods in seen-label, zero-shot and linear-probe settings, while producing substantially fewer hierarchical contradictions.