8464 papers
This paper proposes an AI-driven perception method with hierarchical human intention prediction to enable mobile robot chemists in self-driving laboratories to proactively distinguish between human preparatory actions and transient interactions, thereby overcoming the inefficiencies of passive obstruction detection and streamlining human-robot coordination in shared-access scenarios.
The paper presents CliCooper, a multi-client cooperative Split Learning framework that safeguards data privacy through differential privacy and label obfuscation while ensuring training integrity and model provenance via dynamic chained watermarking, effectively mitigating privacy and ownership attacks without compromising model accuracy.
This paper proposes the AFOP-ML framework, an automatic feature optimization-enabled prototypical network that achieves rapid few-shot tactile recognition of both shapes and materials with high accuracy and robustness against perturbations, effectively addressing the challenges of data scarcity and time-consuming training in robotic applications.
The FoMo dataset presents a comprehensive, multi-season collection of over 64 km of diverse robot navigation data from a boreal forest, featuring significant environmental changes like heavy snow and vegetation growth to challenge and evaluate the robustness of state-of-the-art odometry and SLAM systems.
This paper proposes IMaX, a simple yet effective objective based on the InfoMax principle that maximizes mutual information between learned features and latent labels while mitigating class-balance bias through an -entropic term, thereby significantly improving the performance of state-of-the-art semi-supervised domain generalization methods in long-tailed distribution scenarios.
The paper introduces OLLA, an LLM-driven online aggregation framework that accelerates unstructured text analytics by incrementally processing data and employing semantic stratified sampling to achieve high accuracy with significantly reduced latency compared to full-batch processing.
The paper proposes Alfa, an attentive low-rank filter adaptation method that reweights pre-trained semantic features via singular value decomposition and attention mechanisms to achieve efficient, sample-efficient test-time personalization for cross-domain gaze estimation, outperforming existing methods while demonstrating applicability beyond computer vision.
This paper proposes a Hybrid Evaluation-based Genetic Programming (HE-GP) framework that dynamically switches between exact and approximate evaluation modes within an Online Scheduling Algorithm to efficiently solve the Uncertain Agile Earth Observation Satellite Scheduling Problem, achieving significant computational cost reductions while maintaining superior scheduling performance compared to existing methods.
This report from the ALICE 2026 workshop details the SymBa group's efforts to revive and extend Nils Aall Barricelli's 1953 research on symbiogenesis by replicating his 1D numerical organisms, developing 2D extensions and DNA-norm experiments, and exploring the implications of symbiogenesis for the origins of life, open-ended evolution, and collective intelligence in artificial systems.
This letter proposes and evaluates two operating protocols, mode selection and mode combining, for multi-mode pinching-antenna systems to maximize sum rate in multi-user downlink communications via a jointly optimized PSO-KKT algorithm, demonstrating that mode combining offers superior spectral efficiency while mode selection provides a low-complexity alternative with comparable performance.
The paper proposes R2F, an LLM-free framework for zero-shot open-vocabulary object navigation that repurposes ray frontiers as direction-conditioned semantic hypotheses to achieve competitive performance with real-time execution, eliminating the latency and computational overhead of iterative large-model queries.
LAR-MoE is a two-stage framework that decouples unsupervised skill discovery from policy learning by regularizing expert routing to align with a learned latent representation, enabling robots to achieve high success rates in heterogeneous manipulation tasks without requiring manual skill annotations.
This paper demonstrates that large language model-powered generative agents, enhanced by in-context learning, can effectively relax the rigidity of traditional mathematical models to replicate both rational strategies and systematic behavioral deviations in power dispatch and auction scenarios.
This paper synthesizes drivers of human cybersecurity behavior within organizations into a coherent theoretical framework and proposes its application as a blueprint for securing agentic AI systems against manipulation attacks by drawing parallels between human and AI vulnerabilities.
3PoinTr is a novel method that pretrains robust robot manipulation policies from casual, unconstrained human videos by using a transformer architecture to predict 3D point tracks as an embodiment-agnostic intermediate representation, enabling sample-efficient learning with minimal robot demonstrations.
This paper proposes Visual Self-Fulfilling Alignment (VSFA), a label-free fine-tuning method that shapes safety-oriented personas in multimodal large language models by exposing them to threat-related images during neutral VQA tasks, thereby reducing attack success rates and mitigating over-refusal without compromising general capabilities.
This paper introduces an open-source, robot-agnostic surgical robotics platform featuring a deterministic, closed-form RCM controller and full-stack ROS integration, which achieves sub-millimeter precision and expert-level trajectory smoothness in autonomous laparoscopic tasks across phantom, ex vivo, and in vivo porcine models.
Spherical-GOF is a novel geometry-aware panoramic rendering framework that extends Gaussian Opacity Fields to spherical ray space, achieving superior geometric consistency and photometric quality in 3D scene reconstruction by introducing efficient spherical culling and adaptive filtering to overcome the limitations of existing perspective-based adaptations.
This paper introduces OccTrack360, a new benchmark for 4D panoptic occupancy tracking from surround-view fisheye cameras featuring long, diverse sequences and principled voxel visibility annotations, alongside the proposed Focus on Sphere Occ (FoSOcc) framework that effectively addresses fisheye distortion and localization challenges to establish a strong baseline for future research.
This paper introduces SWIFT, a training-free, few-shot video attribution method that leverages a sliding window reconstruction mechanism to detect generated content with high accuracy across multiple state-of-the-art models without requiring additional training or degrading video quality.