864 papers
SoraNav is a novel framework that enables zero-shot Vision-Language Model reasoning for UAV task-centric navigation by integrating Multi-modal Visual Annotation to encode 3D geometric priors and an Adaptive Decision Making strategy to validate commands, thereby significantly outperforming existing methods in both 2.5D and complex 3D environments.
This paper introduces Dynamic-ICP, a real-time, Doppler-aware registration framework for FMCW LiDAR that robustly estimates ego-motion and aligns scans in highly dynamic environments by integrating point-to-plane geometry with rotation-only Doppler residuals, eliminating the need for external sensors or calibration.
This paper presents a metric, inertially aligned monocular state estimation framework for flexible robotic systems that integrates MLP-learned deformation-force models with continuous-time B-spline kinematics to leverage kinetodynamic priors for robust pose estimation and the recovery of inertial sensing properties like metric scale and gravity.
This paper introduces CERNet, a unified hierarchical predictive-coding recurrent neural network that integrates real-time robot motion generation, online intent recognition, and intrinsic confidence estimation into a single compact framework, demonstrating superior trajectory reproduction and recognition accuracy on a humanoid robot.
This paper demonstrates that training multiple agents to compete in racing tasks with sparse high-level rewards effectively yields agile flight behaviors and strategic capabilities that outperform isolated training methods, offering superior sim-to-real transfer and generalization in complex physical environments.
This paper introduces TOLEBI, a novel learning-based framework that enables fault-tolerant bipedal locomotion by training policies on simulated hardware faults and integrating an online joint status estimation module to classify and adapt to real-time joint conditions during sim-to-real transfer.
This paper presents a fully onboard perception-control pipeline for aerial manipulation that combines an augmented visual-inertial odometry estimator with contact-consistency factors and a hybrid force-motion controller to enable accurate, stable, and deployable contact-rich tasks without relying on external motion capture systems.
This paper proposes H-WM, a Hierarchical World Model that unifies high-level logical reasoning with low-level visual prediction to enable robust, long-horizon robotic task and motion planning while mitigating error accumulation.
The paper introduces PhysMem, a test-time memory framework that enables vision-language model planners to learn and verify physical principles through interaction without updating model parameters, thereby significantly improving manipulation success rates by validating hypotheses before applying them to new scenarios.
FlowCorrect is a modular, interactive imitation learning framework that enables real-time, sample-efficient adaptation of generative flow-matching robotic policies through sparse human pose corrections, significantly improving success rates on previously failed tasks without retraining the underlying model.
This paper introduces LiteVLA-Edge, a deployment-oriented pipeline that enables fully on-device, real-time multimodal control on embedded Jetson Orin hardware by combining FP32 fine-tuning with 4-bit GGUF quantization and GPU-accelerated inference to achieve a 6.6 Hz end-to-end latency within a ROS 2 framework.
This paper presents a multi-agent simulation framework that integrates realistic terrain reconstruction, heterogeneous agent modeling, and reinforcement learning to efficiently simulate and analyze archaeological mobility and transport strategies in complex, uneven landscapes.
This paper introduces Phys4D, a three-stage training pipeline that transforms appearance-driven video diffusion models into physics-consistent 4D world representations by combining pseudo-supervised pretraining, simulation-grounded fine-tuning, and reinforcement learning to achieve fine-grained spatiotemporal and physical consistency.
This paper proposes a real-time, loosely coupled GNSS and IMU integration framework using Factor Graph Optimization that enhances service availability in challenging urban environments by trading off some positioning accuracy for improved computational efficiency compared to traditional batch methods.
This paper presents a real-time, tightly coupled GNSS-IMU integration method based on factor graph optimization that utilizes incremental optimization with fixed-lag marginalization to achieve robust, high-accuracy positioning in dense urban environments, as validated by experiments on the UrbanNav dataset.
The paper introduces Multi-Scale Embodied Memory (MEM), a mixed-modal architecture that integrates video-based short-term and text-based long-term memory to enable vision-language-action models to effectively perform complex, long-horizon robotic tasks spanning up to fifteen minutes.
This paper proposes UrbanHuRo, a two-layer human-robot collaboration framework that jointly optimizes heterogeneous urban services like crowdsourced delivery and sensing through scalable order dispatch and deep reinforcement learning, achieving significant improvements in sensing coverage, courier income, and order timeliness.
This paper presents CoCo-TAMP, a hierarchical state estimation framework that leverages large language models to incorporate common-sense knowledge about object locations and co-occurrence, significantly reducing planning and execution time for robots operating in partially observable environments.
This paper proposes HALyPO, a novel multi-agent reinforcement learning framework that ensures stable and generalizable human-robot collaboration by enforcing Lyapunov-based stability conditions on policy parameters to bridge the rationality gap between heterogeneous agents.
RAGNav is a novel framework for Multi-Goal Visual-Language Navigation that integrates a Dual-Basis Memory system combining topological maps and semantic forests with anchor-guided retrieval and neighbor score propagation to overcome spatial hallucinations and enhance sequential planning efficiency, achieving state-of-the-art performance.