663 papers
This paper presents an infrastructure-less magnetic localization system that enables a lightweight UAV to autonomously track and land with centimeter-level precision on a mobile quadruped robot by fusing real-time magneto-inductive sensing with inertial and optical-flow data, thereby enhancing heterogeneous robot collaboration in unknown environments without external anchors.
This paper introduces PSANE, a framework that enables legged robots to safely navigate and explore unknown deformable granular terrains by leveraging proprioceptive leg-terrain interactions to learn traversability models and optimize frontier selection for real-time, goal-directed navigation.
This paper proposes a model-based image compression technique for tetherless underwater remotely operated vehicles that leverages novel view synthesis priors and gradient descent optimization to achieve superior compression ratios and image quality, particularly in scenarios involving new objects within the scene.
This paper proposes an Adaptive SINDy framework that integrates data-driven Sparse Identification of Non-Linear Dynamics with Recursive Least Squares adaptive control to effectively identify residual forces and reject wind disturbances, demonstrating superior trajectory tracking performance compared to baseline PID and INDI controllers on a lightweight Crazyflie drone in turbulent environments.
This paper introduces NanoBench, an open-source multi-task benchmark dataset collected on a 27g Crazyflie 2.1 nano-quadrotor that provides synchronized actuator-level signals, controller internals, and millimeter-accurate ground truth to address the unique modeling and control challenges of nano-scale aerial robotics.
This paper presents a lightweight 3D LiDAR-based UAV tracking system for small drones that utilizes an Adaptive Extended Kalman Filter to achieve robust, high-accuracy relative positioning in GPS-denied environments by dynamically adjusting to sparse and noisy point cloud data.
This paper introduces PanoAffordanceNet, a novel framework and the first high-quality dataset (360-AGD) designed to enable holistic affordance grounding in 360-degree indoor environments by addressing challenges like geometric distortion and semantic dispersion through distortion-aware calibration and multi-level constraints.
The paper introduces -Occ, a robust 3D semantic occupancy prediction framework that leverages a Multi-view Masked Reconstruction module and a Feature Memory Module to maintain geometric and semantic coherence under incomplete multi-camera inputs, significantly outperforming existing methods in scenarios with missing views.
This study demonstrates that in human-robot interaction, different failure types impact perceived reliability differently—with mistakes being less damaging than slips or lapses—and that trust can be effectively recovered through subsequent successful executions without the need for explicit social repair actions.
This paper addresses the lack of standardized benchmarks and datasets in embodied healthcare by introducing MedMassage-12K, a large-scale multimodal acupoint massage dataset, and proposing HMR-1, a hierarchical framework that leverages vision-language models for high-level acupoint grounding and low-level trajectory control to enable robust robotic massage therapy.
This paper introduces GF-BiLSTM, a two-stream gated fusion network that systematically demonstrates the critical role of incorporating Wi-Fi Channel State Information phase data alongside amplitude to significantly improve the accuracy and cross-speed robustness of robotic activity recognition.
This paper proposes a distributed, formation-aware adaptive conformal prediction framework that generates risk-sensitive uncertainty bounds to enforce visibility constraints and ensure safe, high-performance leader-follower multi-robot operations under heteroscedastic perception errors.
This study investigates how age-related differences in sensitivity to deictic gaze affect social perception during human-robot interaction, aiming to inform the design of adaptive non-verbal cues for social robots assisting older adults.
This paper presents a Safety Enhanced Passivity-Based Nonlinear Model Predictive Control (SEP-NMPC) framework that unifies strict passivity-based stability and high-order control barrier function safety guarantees to enable real-time, collision-free transport of slung payloads by quadrotors in cluttered environments.
This paper presents a novel hierarchical control framework that integrates an indirect adaptive law with model predictive control to enable quadrupedal robots to robustly transport heavy static and dynamic payloads across diverse terrains by estimating unknown parameters and ensuring stability through a convex stability criterion.
This paper proposes a novel, biologically plausible spiking control framework based on the free energy principle, where neurons fire only to reduce internal free energy, achieving efficient and robust performance against both external and internal perturbations while offering new insights for neuromorphic hardware implementation.
This paper addresses the challenge of LiDAR-based 3D semantic segmentation under noisy labels and domain shifts by introducing the DGLSS-NL task, establishing a new benchmark, and proposing DuNe, a dual-view framework that achieves state-of-the-art robustness across multiple datasets.
This paper introduces General Policy Composition (GPC), a training-free method that enhances diffusion and flow-based robot policies by theoretically and empirically demonstrating that convexly combining the distributional scores of multiple pre-trained policies at test time yields superior performance and adaptability across diverse tasks.
This paper introduces the Robot Control Stack (RCS), a lean and modular software ecosystem designed to bridge the gap between large-scale Vision-Language-Action model training and real-world robot deployment by unifying simulation and physical control, while validating its effectiveness through extensive evaluations of policies like Octo, OpenVLA, and Pi Zero.
This paper presents an end-to-end Real2Sim2Real framework for deformable linear object manipulation that employs likelihood-free inference to estimate physical parameter distributions for domain-randomized reinforcement learning, enabling zero-shot deployment of visuomotor policies from simulation to the real world.