IROS 2025 Papers — Page 13
IEEE/RSJ International Conference on Intelligent Robots and Systems · 1984 papers
Motion Planning and Control with Unknown Nonlinear Dynamics through Predicted Reachability
Zhiquan Zhang, M. Ornik
OptimizationRobotic IntelligenceGraph
🎯 What it does: Proposed a hybrid planning-control framework by partitioning the state space, approximating the system as a piecewise affine (PWA) system, and abstracting it as a directed weighted graph. The graph edges are incrementally updated using affine system identification and reachability control theory. Heuristic weights are assigned based on predictive reachability conditions and edge determinism. During task execution, data is adaptively collected and analyzed to update the predictive graph in real-time. Controllers are synthesized online based on graph search results, and the method's effectiveness was validated in a simulation scenario involving mobile robots navigating unknown terrain.
Motion-Feat: Motion Blur-Aware Local Feature Description for Image Matching
Ye Gao, Jinhu Lu
Pose EstimationRetrievalConvolutional Neural NetworkOptical FlowImage
🎯 What it does: Proposed Motion-Feat, an end-to-end motion blur-aware local feature descriptor method.
MotionScript: Natural Language Descriptions for Expressive 3D Human Motions
Payam Jome Yazdian, Angelica Lim
GenerationLarge Language ModelTextMultimodality
🎯 What it does: Develop the MotionScript framework, which generates detailed descriptions of 3D human actions in natural language and uses these descriptions to train text-to-action models.
Motivational Cognitive Maps Allow Robot Biomimetic Autonomy
Oscar Guerrero-Rosado, P. Verschure
Robotic IntelligenceAuto EncoderMultimodality
🎯 What it does: Developed a hippocampus-inspired model called Motivational Hippocampal Autoencoder (MoHA), integrating intrinsic motivation and extrinsic visual information to simulate the firing characteristics of hippocampal place cells under different motivational states, and enable synthetic agents to learn and deploy efficient trajectories in foraging tasks.
Moving Object Segmentation via 3D LiDAR Data: A Learning-Free Real-time Online Alternative
Zinuo Yi, Darius Burschka
SegmentationPoint Cloud
🎯 What it does: Proposed a real-time online, non-learning 3D LiDAR moving object segmentation method
MovSAM: A Single-image Moving Object Segmentation Framework Based on Deep Thinking
Chang Nie, Hesheng Wang
SegmentationTransformerLarge Language ModelPrompt EngineeringImageMultimodalityChain-of-Thought
🎯 What it does: Propose the MovSAM framework to achieve moving object segmentation in single images.
MPC-based Deep Reinforcement Learning Method for Space Robotic Control with Fuel Sloshing Mitigation
Mahya Ramezani, Holger Voos
Robotic IntelligenceReinforcement LearningPhysics Related
🎯 What it does: Proposed a framework combining reinforcement learning (RL) with model predictive control (MPC) for satellite docking control of partially filled fuel tanks, validated its effectiveness on the Zero-G experimental platform and high-precision numerical simulations.
MPDG-SLAM: Motion Probability-Based 3DGS-SLAM in Dynamic Environment
Conghao Huang, Mingrui Li
Object DetectionConvolutional Neural NetworkGaussian SplattingSimultaneous Localization and MappingPoint Cloud
🎯 What it does: Proposes MPDG-SLAM, a 3D Gaussian point cloud rendering SLAM method based on motion probability (MP), integrating mobile-deployable YOLO for dynamic object detection, and assigning labels to Gaussian points via MP attributes, mapping dynamic points to the frontend feature tracking system to correct dynamic interference.
Mr. Virgil: Learning Multi-robot Visual-range Relative Localization
Si Wang, Yue Wang
OptimizationRobotic IntelligenceGraph Neural NetworkSimultaneous Localization and MappingMultimodalityGraph
🎯 What it does: Proposes an end-to-end learning framework named Mr. Virgil, which uses graph neural networks for data association between UWB ranging and visual detection, and combines differentiable pose graph optimization (PGO) to achieve relative localization within the visual range of multi-robot systems.
MRMT-PR: A Multi-Scale Reverse-View Mamba-Transformer for LiDAR Place Recognition
Kan Luo, Xieyuanli Chen
RecognitionTransformerPoint Cloud
🎯 What it does: Proposed a multi-scale inverse perspective Mamba-Transformer architecture MRMT-PR for place recognition based on LiDAR single-frame point clouds.
MRS-CWC: A Weakly Constrained Multi-Robot System with Controllable Constraint Stiffness for Mobility and Navigation in Unknown 3D Rough Environments
Runze Xiao, H. Asama
Robotic Intelligence
🎯 What it does: Proposed a tunable weakly constrained multi-robot system (MRS-CWC) that achieves a balance between flexibility and maneuverability among robots through dynamically adjustable constraint stiffness, enabling navigation in unknown 3D rugged environments.
MSPA-LIO: LiDAR-Inertial Odometry with Multi-Scale Plane Adjustment
Su Yan, Song Wu
Autonomous DrivingSimultaneous Localization and MappingPoint Cloud
🎯 What it does: Proposed MSPA-LIO, a LiDAR-Inertial odometry method with multi-scale plane adjustment; by applying geometric constraints and plane adjustment at both voxel plane scale and large-scale plane scale, and employing voxel-based explicit large plane extraction to fully utilize planar structures in the environment, using large planes to correct the orientation of related voxel planes, thereby improving the consistency between pose and map.
MT-Fusion: Multi-Task Learning for Degradation-Aware Infrared and Visible Image Fusion
Yuyang Gao, Kenji Hashimoto
RestorationRobotic IntelligenceImageMultimodality
🎯 What it does: Proposed an MT-Fusion deep learning workflow based on multi-task learning for fusing infrared and visible light images under various degradation scenarios, and automatically identifying degradation types to select the corresponding encoder.
Multi-Agent Combinatorial Path Finding for Tractor-Trailers in Occupancy Grids
Xuemian Wu, Zhongqiang Ren
Autonomous DrivingOptimization
🎯 What it does: Propose and solve the multi-agent cooperative path planning problem, conducting collision-free path search and optimizing arrival time for the tractor-trailer model
Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds
Rohan Chandra, Joydeep Biswas
Reinforcement LearningSequential
🎯 What it does: Proposed a multi-agent maximum entropy inverse reinforcement learning algorithm tailored for real-world unstructured crowds;
Multi-Agent Pickup and Delivery with Mobile Pickups
Benedetta Flammini, Bruno Lacerda
Optimization
🎯 What it does: Proposed an extended algorithm called TP-EL based on Token Passing to handle item exchange between suppliers and deliverers, addressing the multi-agent pickup and delivery problem (MAPD) with mobile pickup.
Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications
Jiangwei Wang, Fei Miao
Reinforcement Learning
🎯 What it does: Proposed a multi-agent reinforcement learning framework based on Signal Temporal Logic (STL), guiding the learning process by using the robustness value of STL specifications for reward design.
Multi-Agent Reinforcement Learning with Transformer-based Spatio-temporal Fusion for Autonomous Driving in Mixed Traffic
Rixin Li, Tiantian Xu
Autonomous DrivingTransformerReinforcement LearningContrastive Learning
🎯 What it does: A multi-agent reinforcement learning method combining a Transformer-based spatiotemporal fusion module (TSTF) with auxiliary contrastive learning tasks is studied for autonomous driving decision-making in mixed traffic environments
Multi-Cali Anything: Dense Feature Multi-Frame Structure-from-Motion for Large-Scale Camera Array Calibration
Jinjiang You, W. Pu
OptimizationImage
🎯 What it does: Propose a multi-frame structured light reconstruction (SfM) camera array calibration method based on dense features, which directly optimizes camera intrinsic parameters from scene data without requiring additional calibration pattern capture.
Multi-Functional Granular Propulsion: Bio-Inspired Orientation Control and Local Fluidization for Crawl-To-Dig Transitions
Dongting Li, Nick Gravish
Robotic IntelligencePhysics Related
🎯 What it does: Adding adjustable body anchors to existing spiral propulsion vehicles (SPV) to achieve directional control and switching between horizontal crawling and vertical digging, and studying the impact of localized liquefaction technology on performance.
Multi-Material 3D-Printed Magnetic Millirobot for Quadrupedal Locomotion in Endoluminal Spaces
Ruichen Wang, Dong Wang
Robotic Intelligence
🎯 What it does: Developed a four-legged magnetic microrobot (beamrobot) fabricated using multi-material direct ink writing (DIW), featuring a flexible body equipped with magnetic feet that enable shape transformation and motion under an external magnetic field;
Multi-Modal Graph Convolutional Network with Sinusoidal Encoding for Robust Human Action Segmentation
Hao Xing, Gordon Cheng
SegmentationGraph Neural NetworkMultimodality
🎯 What it does: Proposes a multi-modal graph convolutional network (MMGCN) that fuses low-frame-rate visual data with high-frame-rate motion data (skeleton and object detection) to achieve robust human action temporal segmentation.
Multi-Objective Optimization of Humanoid Robot Hardware and Control for Multiple Tasks via Genetic Algorithms
Carlotta Sartore, Daniele Pucci
OptimizationRobotic Intelligence
🎯 What it does: Jointly optimize the hardware structure and hierarchical control parameters of a humanoid robot using Non-dominated Sorting Genetic Algorithm II (NSGA-II) to enhance multi-task performance in walking and carrying.
Multi-PrefDrive: Optimizing Large Language Models for Autonomous Driving Through Multi-Preference Tuning
Yun Li, Manabu Tsukada
Autonomous DrivingTransformerLarge Language ModelSupervised Fine-Tuning
🎯 What it does: Proposed the Multi-PrefDrive framework, which enhances autonomous driving performance based on large language models by pairing each selected action with multiple rejected alternative actions and employing the Plackett-Luce preference model for multi-dimensional preference tuning.
Multi-Robot Assembly of Deformable Linear Objects Using Multi-Modal Perception
Kejia Chen, Rüdiger Daub
Robotic IntelligenceMultimodality
🎯 What it does: Propose a multi-robot assembly framework based on multi-modal perception, completing the entire process from picking up to assembly;
Multi-Robot Coordination in an Adversarial Graph-Traversal Game
J. Berneburg, Daigo Shishika
OptimizationRobotic IntelligenceReinforcement LearningGraph
🎯 What it does: Studies coordinated behaviors of robots in adversarial graph traversal games, establishes a non-cooperative stochastic game model, solves for Nash equilibrium, and conducts theoretical analysis to provide performance bounds.
Multi-Robot Ergodic Trajectory Optimization with Relaxed Periodic Connectivity
Yongce Liu, Zhongqiang Ren
OptimizationRobotic Intelligence
🎯 What it does: Proposes a multi-robot trajectory planning method combining intermittent connectivity maintenance with traversable search for information gathering tasks.
Multi-Robot Motion Planning with Cooperative Localization
Anne Theurkauf, Morteza Lahijanian
Robotic Intelligence
🎯 What it does: Proposes a multi-robot cooperative localization motion planning problem under the presence of motion and measurement noise, and presents an algorithm with safety guarantees
Multi-Sets Trees (MST*): Accelerated Asymptotically Optimal Motion Planning Optimization Informed by Multiple Domain Subsets
Liding Zhang, A. Knoll
OptimizationRobotic Intelligence
🎯 What it does: Proposed and implemented a sampling-based motion planner based on multi-set tree (MST*), utilizing GuILD sets and Beacon selectors to accelerate path search and optimization;
Multi-Step Deep Koopman Network (MDK-Net) for Vehicle Control in Frenet Frame
Mohammad Abtahi, S. Nazari
Autonomous DrivingOptimization
🎯 What it does: Propose a deep learning-based Koopman model that describes the full dynamics of a vehicle from pedal and steering inputs to chassis states in the Frenet coordinate system, and apply it to MPC control.
Multi-target Association and Localization with Distributed Drone Following: A Factor Graph Approach
Kaixiao Ye, Tao Yang
Object TrackingAutonomous DrivingOptimizationRobotic Intelligence
🎯 What it does: A factor graph-based multi-target association and localization method is proposed, integrating sensor measurements and control constraints from distributed UAVs to achieve joint association and localization of multiple targets. The effectiveness and robustness of the method are validated through simulations and real-world experiments.
Multi-UAV Deployment in Obstacle-Cluttered Environments with LOS Connectivity
Yuda Chen, Meng Guo
Optimization
🎯 What it does: Propose an efficient restricted search method based on minimal edge RRT* to find a spanning tree topology requiring the fewest drones; and propose a distributed model predictive control (MPC) strategy to achieve online motion coordination, ensuring collision safety and line-of-sight connectivity among drones.
Multi-UAV Formation Control with Static and Dynamic Obstacle Avoidance via Reinforcement Learning
Yuqing Xie, Yu Wang
Robotic IntelligenceTransformerReinforcement Learning
🎯 What it does: A two-phase reinforcement learning process is used to achieve multi-drone formation control, with obstacle avoidance implemented in both static and dynamic obstacle environments.
Multi-UAV-UGV Collision-Free Tracking Control via Control Barrier Function-Based Reinforcement Learning
Haojie Xia, Housheng Su
Autonomous DrivingOptimizationReinforcement Learning
🎯 What it does: Propose a three-tier hierarchical control scheme to achieve multi-unmanned aerial vehicle (UAV)-unmanned ground vehicle (UGV) cooperative tracking, including feature matching, reinforcement learning (RL) real-time tracking, and collision avoidance based on control barrier functions.
Multi-View Normal and Distance Guidance Gaussian Splatting for Surface Reconstruction
Bo Jia, Lin Cao
Gaussian SplattingImage
🎯 What it does: Propose a multi-view regularized Gaussian Splatting method, achieving geometric depth unification and high-precision surface reconstruction for small indoor and outdoor scenes through a distance projection re-projection regularization module and a multi-view normal enhancement module.
Multimodal Anomaly Detection with a Mixture-of-Experts
C. Willibald, Dongheui Lee
Anomaly DetectionRobotic IntelligenceMixture of ExpertsVision Language ModelMultimodality
🎯 What it does: Proposed a hybrid expert framework that fuses vision-language models and Gaussian Mixture Regression detectors to achieve multimodal anomaly detection
Multimodal Autonomous Robotic Long-Horizon Task Planning via Embodied Language Model and Behavior Trees
Hongpeng Chen, Pai Zheng
Robotic IntelligenceTransformerLarge Language ModelVision Language ModelMultimodality
🎯 What it does: Propose a multimodal framework based on large language models and vision-language models for planning in long-term robotic manipulation tasks.
Multimodal Deformation Estimation of Soft Pneumatic Gripper During Operation
Changheng Cai, Yuan Gao
Representation LearningRobotic IntelligenceMultimodality
🎯 What it does: Proposed a multi-modal learning-based sensing method that integrates a camera and inertial measurement unit (IMU) for real-time perception of whole-body deformation of soft pneumatic grippers.
Multimodal Human Activity Recognition with a Large Language Model for Enhanced Human-Robot Interaction
G. Khodabandelou, Y. Amirat
RecognitionRobotic IntelligenceTransformerLarge Language ModelMultimodality
🎯 What it does: Propose a framework that unifies all sensor streams (visual, audio, inertial) into the text domain and directly uses GPT-3 for multimodal activity recognition;
Multimodal Integrated Prediction and Decision-making with Adaptive Interaction Modality Explorations
Tong Li, Shaojie Shen
Autonomous DrivingWorld ModelMultimodality
🎯 What it does: Proposed the MIND framework, which can simultaneously generate predictions and decisions for multi-modal interactions.
Multimodal Obstacle Detection and Adaptive Neural Control for Autonomous Drones
Theerawath Phetpoon, P. Manoonpong
Object DetectionAutonomous DrivingRobotic IntelligenceImageMultimodalityPoint Cloud
🎯 What it does: Proposed an integrated multi-modal obstacle detection and adaptive neural control unmanned aerial vehicle (UAV) system capable of autonomous navigation during both day and night.
Multimodal Point Cloud Registration Method Based on Centerline-Guided Expansion and Contraction: An Optimization Strategy Applied in Bronchial Lumen Map Building
Le Ren, Lining Sun
OptimizationSimultaneous Localization and MappingMultimodalityPoint CloudComputed Tomography
🎯 What it does: Proposes a multi-modal point cloud registration method that utilizes CT and intraoperative video frames, leveraging centerline guidance for bronchial cavity modeling.
Multimodal Upstream Motion of Magnetically Controlled Micro/Nano Robots in High-Viscosity Fluids
Chan Li, Lin Feng
Robotic IntelligenceBiomedical DataPhysics Related
🎯 What it does: Study the effects of high blood viscosity and blood cell interference on the upstream movement of magnetic microrobots, combining theoretical modeling, simulation, and experimental validation, and derive a velocity formula for microrobots in non-Newtonian fluids.
MultiNash-PF: A Particle Filtering Approach for Computing Multiple Local Generalized Nash Equilibria in Trajectory Games
Maulik Bhatt, Negar Mehr
OptimizationComputational Efficiency
🎯 What it does: Propose the MultiNash-PF algorithm, which uses the particle filter method to compute multiple local generalized Nash equilibria in multi-objective trajectory games.
Multiphysics Model and Hysteresis Compensation for Control of Electroactive Actuators
Ferradj Imane, N. Mechbal
OptimizationPhysics Related
🎯 What it does: Propose a model-based control architecture that combines an analytically inverted generalized Prandtl-Ishlinskii hysteresis model with model predictive control (MPC) to linearize the nonlinear hysteresis of PVDF-TrFE-CTFE polymer actuators and optimize control inputs under actuator constraints; validate the method through experimental measurements of actuator deflection and electric displacement, as well as numerical simulations;
Multiple Object Tracking with Dynamic Adaptive Object Motion Estimation
B. Cheng, Yunzhou Zhang
Object TrackingAutonomous DrivingImageMultimodalityPoint Cloud
🎯 What it does: Proposed the DA-MOT multi-object tracking algorithm, which calculates the dynamic and static states of objects using LiDAR and camera information, dynamically adjusts Kalman filter parameters based on object motion, and designs a re-association mechanism to correct incorrect ID associations.
Multiple-scale augmented reality markers for positioning of robotic micromanipulation
Shuzhang Liang, Fumihito Arai
Pose EstimationRobotic IntelligenceImage
🎯 What it does: This study designs and implements multi-scale augmented reality markers for localizing robotic manipulators at different scales, and ultimately performs cell loading operations at the end of the microfluidic chip through camera intrinsic calibration, marker detection, and distance compensation.
Multistream Network for LiDAR and Camera-based 3D Object Detection in Outdoor Scenes
Muhammad Ibrahim, Ajmal Mian
Object DetectionAutonomous DrivingConvolutional Neural NetworkImagePoint CloudBenchmark
🎯 What it does: Propose a multi-stream network (MuStD) for fusing LiDAR and RGB data to enhance outdoor 3D object detection accuracy.
Multitask Reinforcement Learning for Quadcopter Attitude Stabilization and Tracking using Graph Policy
Y. Liu, M. Basiri
OptimizationRobotic IntelligenceGraph Neural NetworkReinforcement Learning
🎯 What it does: Proposed a multi-task deep reinforcement learning framework for quadrotor attitude tracking and aggressive stabilization from arbitrary states; the framework leverages IsaacGym parallel simulation and graph convolutional network (GCN) strategies to unify the processing of two tasks, achieving 400 Hz control on Pixhawk flight controllers;
Muscle-on-a-Chip: A Self-Healing Actuator Platform in Robotic Systems*
Hongze Yin, Tao Yue
Robotic IntelligenceBiomedical Data
🎯 What it does: Designed and implemented a programmable microfluidic chip platform to construct functional muscle tubes using C2C12 cells, and simulated exercise-induced muscle damage and its regeneration process through controllable strain.
MuSPaCSA: Multi-Scale Parallel-Channel Self-Attention Network for Point Cloud Classification and Segmentation
Xuran Yao, Xutao Li
ClassificationSegmentationTransformerPoint Cloud
🎯 What it does: Proposed the MuSPaCSA network for point cloud classification and segmentation tasks, employing a multi-scale parallel channel self-attention mechanism.
MuxHand: A Cost-Effective and Compact Dexterous Robotic Hand Using Time-Division Multiplexing Mechanism
Jianle Xu, Chongkun Xia
Robotic Intelligence
🎯 What it does: Proposed a low-cost, compact Dexterous Robotic Hand (MuxHand) using a time-division multiplexing motor mechanism, and verified its grasping and manipulation performance through experiments.
MV2: A Large-Scale 360-degree Multi-View Maritime Vision Dataset for Object Detection and Segmentation
Junseok Lee, Kyoobin Lee
Object DetectionSegmentationImageBenchmark
🎯 What it does: Constructed and released the Multi-View Maritime Vision (MV2) dataset, and conducted benchmark tests on existing object detection and panoptic segmentation models on it.
Mysteric-Net: MIMO Hysteretic Friction-aware Lagrangian-based Network for Legged Robot
Hoyeong Yeo, Sehoon Oh
Robotic Intelligence
🎯 What it does: Proposes Mysteric-Net, a MIMO hysteresis friction perception network that integrates the Lagrangian formulation and TCN, for accurately identifying robot leg dynamics and enhancing inverse dynamics estimation and tracking performance.
NailTact: Single-camera based Tactile Fingertip with Nail
Hao Zhou, Kazuhiro Shimonomura
Robotic IntelligenceImage
🎯 What it does: Designed and verified the NailTact fingertip tactile sensor based on a single camera, which can simultaneously detect forces applied to both the fingertip and the nail. The response characteristics of the sensor were validated through experiments with a robotic finger prototype under conditions of nail load, grasping thin objects, and touching a tabletop. Additionally, a simplified model was proposed to explain the relationship between nail force and marker displacement.
NanoMVG: USV-Centric Low-Power Multi-Task Visual Grounding based on Prompt-Guided Camera and 4D mmWave Radar
Runwei Guan, Yutao Yue
Object DetectionSegmentationComputational EfficiencyRobotic IntelligencePrompt EngineeringImageMultimodality
🎯 What it does: Designed and implemented a low-power multi-task visual localization model, NanoMVG, for unmanned surface vessels (USV) to perform box-level and mask-level visual localization tasks simultaneously through natural language guidance using a camera and 4D millimeter-wave radar in aquatic environments.
Natural Humanoid Robot Locomotion with Generative Motion Prior
Haodong Zhang, Rong Xiong
Robotic IntelligenceAuto Encoder
🎯 What it does: Proposed a Generative Motion Prior (GMP) that generates natural reference actions through full-body motion remapping and conditional variational autoencoders, and provides fine-grained trajectory-level supervision during policy training using a frozen online motion generator.
NavHD: Low-Power Learning for Micro-Robotic Controls in the Wild
Chae Young Lee, Zerina Kapetanovic
Computational EfficiencyRepresentation LearningRobotic IntelligenceReinforcement Learning
🎯 What it does: Proposed and implemented NavHD, a low-power learning model suitable for micro-robot navigation
Navi2Gaze: Leveraging Foundation Models for Navigation and Target Gazing
Jun Zhu, Tao Zhang
Autonomous DrivingOptimizationVision Language ModelBenchmark
🎯 What it does: Proposed a navigation and target gaze method called Navi2Gaze based on vision-language models, designed to automatically select the optimal posture in open-vocabulary tasks.
NaviDiffuser: Tackling Multi-Objective Robot Navigation by Weight Range Guided Diffusion Model
Xuyang Zhang, Jianmin Ji
Knowledge DistillationRobotic IntelligenceTransformerDiffusion model
🎯 What it does: Introduce diffusion models for multi-robot navigation, using classification labels to guide the model in learning the relationship between navigation and human preferences.
NaviFormer: A Deep Reinforcement Learning Transformer-like Model to Holistically Solve the Navigation Problem
Daniel Fuertes, N. García
Autonomous DrivingTransformerReinforcement Learning
🎯 What it does: Proposed and implemented a deep reinforcement learning model called NaviFormer, which utilizes the Transformer architecture to simultaneously predict high-level route planning and low-level trajectory planning, thereby addressing the global navigation problem.
NeuFlow-V2: Push High-Efficiency Optical Flow To the Limit
Zhiyong Zhang, H. Singh
Computational EfficiencyConvolutional Neural NetworkOptical Flow
🎯 What it does: Propose NeuFlow-V2, a method for real-time high-precision optical flow estimation, which includes a lightweight backbone network and a fast refinement module.
Neural Collision Detection for Constrained Grasp Pose Optimization in Cluttered Environments
Longyuan Lin, Yuanlong Yu
OptimizationRobotic Intelligence
🎯 What it does: Proposes a framework that integrates collision avoidance as a core constraint into grasp pose optimization for achieving robust robot grasping in cluttered environments;
Neural Configuration Distance Function for Continuum Robot Control
Kehan Long, Nikolay Atanasov
OptimizationRobotic Intelligence
🎯 What it does: Proposed and implemented a neural network-based shape modeling method for continuum robots, N-CSDF, and integrated it into the MPPI controller to generate safe trajectories.
Neural MP: A Neural Motion Planner
Murtaza Dalal, Deepak Pathak
Autonomous DrivingOptimizationKnowledge Distillation
🎯 What it does: Proposed a neural motion planner called Neural MP, which utilizes a large amount of complex scenarios constructed in simulations to collect expert data, distills them into reactive neural policies, and subsequently combines them with lightweight optimization methods to achieve safe path planning in real environments.
Neural network control method for target tracking of magnetically actuated capsule endoscopic robots with obstacle avoidance and noise-resistant capabilities
Zhiwei Cui, Zheng Li
Robotic Intelligence
🎯 What it does: A target tracking neural network control method for a magnetic-controlled capsule endoscopy robot with obstacle avoidance and noise suppression capabilities was designed.
Neural Signatures and Decoding of the Various Cognitive Processes Elicited by the Same Stimulus
Jiawei Ju, Yongjie Zou
ClassificationBiomedical Data
🎯 What it does: Constructed an experimental paradigm for different cognitive processes under the same stimulus, explored their neural features, and decoded multiple cognitive processes using EEG signals.
Neural-Link: Non-Overlapping MPC Fusion and Passive Inertial Sensing on Soft Platforms
Zijia Dai, Laurent Kneip
Robotic IntelligenceSimultaneous Localization and MappingImageTime Series
🎯 What it does: This paper proposes a method that integrates an elastic deformation model into sensor fusion on a soft elastic platform. By using a neural network to map temporal deformation sequences to mass-normalized restoring forces, and leveraging consistency constraints between a continuous-time trajectory model and Newton's second law, the method achieves camera alignment, scale recovery, and inertial alignment on a non-overlapping stereo camera system with elastic connections.
Neural-Lyapunov Fusion: Stable Dynamical System Learning for Robotic Motion Generation
Haoyu Zhang, Long Cheng
Robotic Intelligence
🎯 What it does: Proposed a novel adaptive dynamic system (ADS) algorithm utilizing neural network technology, achieving point-to-point and periodic motion learning with stability guarantees through a neural Lyapunov function;
NeuroLoc: Encoding Navigation Cells for 6-DOF Camera Localization
Xun Li, Xian Wei
Pose EstimationTransformerImage
🎯 What it does: Proposed a neurobiologically inspired camera localization method called NeuroLoc, which leverages Hebbian learning modules, head-direction cell-inspired multi-head attention embedding, and 3D grid center prediction to enhance the robustness and accuracy of single-image localization.
Never too Prim to Swim: An LLM-Enhanced RL-based Adaptive S-Surface Controller for AUVs under Extreme Sea Conditions
Guanwen Xie, Yi Li
OptimizationRobotic IntelligenceTransformerLarge Language ModelReinforcement Learning
🎯 What it does: Developed an adaptive S-surface controller for AUVs based on LLM-enhanced reinforcement learning;
New Network Protocol for Supermedia-Enhanced Telerobotics
Xinyu Liu, Ning Xi
OptimizationRobotic Intelligence
🎯 What it does: Propose a network transmission protocol TRCP for hypermedia-enhanced remote robot operation
Nezha-Morphing: Design and Experiments of a Seabird-Inspired Hybrid Aerial Underwater Vehicle
Muxierepu Aili, Lian Lian
🎯 What it does: Designed and tested a folding-arm hybrid aerial-aquatic drone named Nezha-Morphing that mimics a seabird.
Nezha-T: a Bi-floating State Lightweight Tail-sitter HAUV
Xiqiao Han, Lian Lian
Robotic Intelligence
🎯 What it does: Designed and verified an ultra-lightweight bistable floating state hybrid air-sea vehicle named Nezha-T
NGD-SLAM: Towards Real-Time Dynamic SLAM without GPU
Yuhao Zhang, Mikel Luján
Pose EstimationSimultaneous Localization and MappingOptical Flow
🎯 What it does: Propose a real-time dynamic SLAM system that runs on CPU only, combining mask propagation mechanism and hybrid tracking strategy
Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environments
Jiawen Yu, Wenqiang Zhang
Anomaly DetectionKnowledge Distillation
🎯 What it does: Proposes a noise-fusion distillation learning framework specifically designed for anomaly detection and localization in complex industrial environments.
NOLO: Navigate Only Look Once
Bohan Zhou, Zongqing Lu
Autonomous DrivingReinforcement LearningOptical FlowVideo
🎯 What it does: Proposes a method that learns navigation policies using only offline videos, which infers pseudo action labels through optical flow and is trained with offline reinforcement learning;
Non-Buoyant Microrobots Swimming with Near-Zero Angle of Attack
Leendert-Jan W. Ligtenberg, Islam S. M. Khalil
Robotic Intelligence
🎯 What it does: Analyzes and experimentally verifies a control method for magnetic-driven helical microrobots to achieve drift-free near-zero angle linear movement at low Reynolds numbers.
Non-Contact Hand-Guided Coarse Positioning of Neurosurgical Instrument Insertion End Effector Based on Magnetic Sensing
Yitian Xian, Zheng Li
OptimizationRobotic Intelligence
🎯 What it does: Proposed a non-contact hand guidance method based on magnetic sensing for the coarse positioning of the instrument insertion end effector (IIEE) in neurosurgical procedures.
Non-differentiable Reward Optimization for Diffusion-based Autonomous Motion Planning
Giwon Lee, Kuk-Jin Yoon
Autonomous DrivingOptimizationReinforcement LearningDiffusion model
🎯 What it does: Proposed a reinforcement learning-based training scheme enabling diffusion models to learn non-differentiable safety and effectiveness objectives, and introduced a reward-weighted dynamic threshold algorithm to construct dense reward signals.
Non-Overlap-Aware Egocentric Pose Estimation for Collaborative Perception in Connected Autonomy
Hongjie Huang, Peng Gao
Pose EstimationAutonomous DrivingTransformer
🎯 What it does: Proposed a NOPE method for self-coordinate system pose estimation under non-overlapping perspectives in multi-robot collaborative perception.
Nonlinear Viscoelastic Model-based Deformation Optimization for Robotic Micropuncture in Retinal Vein Cannulation
Bo Hu, Xin Zhao
OptimizationRobotic IntelligenceBiomedical Data
🎯 What it does: Proposed a robot micropuncture scheme based on a nonlinear viscoelastic model, achieving deformation optimization of retinal vessels through a preload strategy, NV model, and speed optimization framework;
Normalized Triangulation for Calibrated Dual-View 3D Human Pose Estimation
Zijian Zhang, Tianyi Ma
Pose EstimationImage
🎯 What it does: Propose decomposing dual-camera 3D human pose estimation into 2D pose estimation and 2D→3D lifting, and introduce a novel technical solution for the latter.
Novel Cable Driven Fitness Gym Devices for Whole Body Weight Training
J. Park, Seungyong Hyung
Biomedical Data
🎯 What it does: Developed cable-driven wearable and fixed exercise equipment targeting lower limb muscles and full-body strength training, respectively, with interchangeable drive modules; evaluated their effectiveness through surface electromyography (sEMG) experiments compared to traditional dumbbells.
Novel Data-Driven Repetitive Motion Control Scheme for Redundant Manipulators With Zeroing Neurodynamics
Min Yang, Hui Zhang
Data-Centric LearningRobotic Intelligence
🎯 What it does: Proposed a data-driven discrete zeroing neural dynamics (DDZN) model and developed a data-driven Jacobian matrix estimation method, achieving trajectory tracking and repeatable configuration recovery for redundant manipulators without relying on structural parameters;
Novel Diffusion Models for Multimodal 3D Hand Trajectory Prediction
Junyi Ma, Hesheng Wang
GenerationTransformerDiffusion modelImageTextMultimodalityPoint Cloud
🎯 What it does: Propose the MMTwin model, which utilizes multimodal inputs (2D RGB, 3D point clouds, historical hand trajectory, and text prompts) for 3D hand trajectory prediction.
Novel LPV System Identification for a Gantry Stage: A Global Approach with Adjustable Basis Functions
Jegwon Yoon, Sehoon Oh
OptimizationComputational EfficiencyRobotic Intelligence
🎯 What it does: Proposed a global LPV system identification method for multi-axis robot gantry frames, enabling independent selection of basis function orders for each parameter to eliminate unnecessary high-order terms.
NuExo: A Wearable Exoskeleton Covering all Upper Limb ROM for Outdoor Data Collection and Teleoperation of Humanoid Robots
Rui Zhong, Huimin Lu
Robotic IntelligenceMultimodality
🎯 What it does: Developed a wearable upper-limb full range of motion (ROM) exoskeleton system with immersive remote control and multimodal sensing for outdoor data collection and humanoid robot teleoperation.
Numerical Optimization-based Kinematics with Pose Tracking Control for Continuum Robots
Rui Peng, Peng Lu
OptimizationRobotic IntelligenceTime Series
🎯 What it does: Using multiple IMUs to record the pose data of three consecutive robotic arm end-effectors, develop a coordinate transformation scheme, propose a numerically optimized unified forward and inverse kinematics model, and utilize real-time attitude feedback to achieve closed-loop control. Finally, validate the model and controller's accuracy and convergence performance in simulation and real robot experiments.
NUSense: Shear Based Robust Optical Tactile Sensor
Madina Yergibay, Tasbolat Taunyazov
Robotic IntelligenceImage
🎯 What it does: This paper proposes and verifies an optical tactile sensing principle based on shear strain detection, using a dyed silicone gel layer to quantify shear displacement, and employing a NUSense camera to capture in real-time the stretching of the soft pad under mechanical loading (Poisson effect); subsequently, the robustness of the sensor was tested through multiple 8 N load cycles (5 mm radius ball-ended indenter) on the outer layer.
OASIS: Real-Time Opti-Acoustic Sensing for Intervention Systems in Unstructured Environments
Amy Phung, Richard Camilli
Robotic IntelligenceImageMultimodalityAudio
🎯 What it does: Real-time 3D reconstruction in unstructured underwater workspace through the fusion of optical images and acoustic data
Object Extrinsic Contact Surface Reconstruction through Extrinsic Contact Sensing from Visuo-tactile Measurements
Yoonjin Kim, Jung Kim
Multimodality
🎯 What it does: Developed a framework for external contact surface reconstruction based on vision-based tactile sensing, capable of identifying and estimating points and line contact positions where objects interact with the environment.
Object Packing and Scheduling for Sequential 3D Printing: a Linear Arithmetic Model and a CEGAR-inspired Optimal Solver
Pavel Surynek, Petr Kubis
Optimization
🎯 What it does: Propose to formulate the sequential object placement and scheduling problem in 3D printing as a linear arithmetic formula, and improve solving efficiency by utilizing SMT solvers along with CEGAR-inspired abstraction refinement techniques.
Observability Investigation for Rotational Calibration of (Global-pose aided) VIO under Straight Line Motion
Junlin Song, M. Olivares-Méndez
Pose EstimationSimultaneous Localization and Mapping
🎯 What it does: Analyzes the observability of rotation extrinsic parameters between IMU and camera under linear motion (pure translational linear motion), proving that it leads to at least one degree of freedom being unobservable.
Observability-driven Assignment of Heterogeneous Sensors for Multi-Target Tracking
Seyed Ali Rakhshan, He Kong
Object TrackingOptimization
🎯 What it does: Proposed a heterogenous sensor allocation algorithm based on observability for multi-target tracking
Observation of Snails and a Bionic Snail Robot Crawling with Distributed Suction
Qinjie Ji, Josie Hughes
Robotic Intelligence
🎯 What it does: Designed and implemented a snail-inspired robot that combines a spiral propeller and distributed suction cups to achieve upside-down crawling on ceilings.
Observation-Graph Interaction and Key-Detail Guidance for Vision and Language Navigation
Yifan Xie, Yaohua Liu
Autonomous DrivingGraph Neural NetworkVision-Language-Action ModelMultimodality
🎯 What it does: Proposes the Observation-Graph Interaction and Key-Detail Guidance (OIKG) framework for Visual and Language Navigation (VLN), enhancing navigation performance through the Observation-Graph Interaction module and the Key-Detail Guidance module.
Occlusion-Aware 6D Pose Estimation with Visual Observation Guided Diffusion Model
Yanbin Xiong, Jun Cheng
Pose EstimationDiffusion modelImage
🎯 What it does: Estimate the 6D pose of target objects in RGB-D images through occlusion-aware guidance based on diffusion models, and adaptively refine the pose in occluded and cluttered scenes.
Occupancy-belief Planning of Plant Manipulation for Staking
Pusong Li, Rakesh Nagi
Robotic IntelligenceWorld ModelImageAgriculture Related
🎯 What it does: Developed a plant fixation grasping system utilizing occupancy-belief planning.
OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework
Jingyu Song, Katherine A. Skinner
Data SynthesisRobotic IntelligenceImagePhysics Related
🎯 What it does: Propose OceanSim, a high-fidelity GPU-accelerated underwater simulation framework that utilizes advanced physical rendering techniques to reduce the simulation-to-reality gap, achieving real-time imaging sonar rendering and rapid synthetic data generation.