IROS 2025 Papers — Page 14
IEEE/RSJ International Conference on Intelligent Robots and Systems · 1984 papers
Offline Imitation Learning upon Arbitrary Demonstrations by Pre-Training Dynamics Representations
Haitong Ma, Na Li
Representation LearningReinforcement LearningContrastive Learning
🎯 What it does: Propose introducing a pre-training phase in offline imitation learning to enhance performance when expert data is limited by learning dynamic representations.
Offline motion tracking of multi-link mechanisms using inertial sensor fusion and EKF-preconditioned FGO
Aderajew Tilahun, G. Dissanayake
OptimizationRobotic IntelligenceTime Series
🎯 What it does: This paper proposes an offline spatial motion estimation method for multi-link mechanisms based on IMU sensor fusion.
Offline Reinforcement Learning with Koopman Operators for Control of Soft Robots
Yue Jiang, Xinglong Zhang
Robotic IntelligenceReinforcement Learning
🎯 What it does: Proposes a soft robot control framework based on the Koopman operator and offline reinforcement learning, capable of generating control policies without requiring a physical simulator or real-world interaction;
Omni-Scan: Creating Visually-Accurate Digital Twin Object Models Using a Bimanual Robot with Handover and Gaussian Splat Merging
Tianshuang Qiu, Kenneth Y. Goldberg
Anomaly DetectionRobotic IntelligenceGaussian SplattingOptical FlowImage
🎯 What it does: Proposed the Omni-Scan pipeline, utilizing dual-robotic hands for grasping and handover, combined with a single fixed camera to rotate and re-grasp objects, achieving 360° multi-view image acquisition without occlusion, and generating high-quality 3D Gaussian Splatting (3DGS) digital twin models through model training.
OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB
Yunzhi Lin, Kevin J. Liang
Object TrackingPose EstimationImageBenchmark
🎯 What it does: Proposed a large synthetic dataset Omni-Pose6D, a benchmark evaluation framework, and designed a keypoint refinement network that utilizes uncertainty modeling.
On Learning Closed-Loop Probabilistic Multi-Agent Simulator
Juanwu Lu, Ziran Wang
Autonomous DrivingWorld ModelImagePoint Cloud
🎯 What it does: Proposed a probabilistic multi-agent simulation framework based on a hierarchical Bayesian model, Neural Interactive Agents (NIVA), achieving closed-loop, observation-conditioned autoregressive sampling;
On learning racing policies with reinforcement learning
Grzegorz Czechmanowski, Krzysztof Walas
Autonomous DrivingReinforcement Learning
🎯 What it does: Proposes a method using reinforcement learning to learn autonomous racing strategies
On the Analysis of Stability, Sensitivity and Transparency in Variable Admittance Control for pHRI Enhanced by Virtual Fixtures
Davide Tebaldi, L. Biagiotti
Robotic Intelligence
🎯 What it does: Analyze the instability sources of proxy-type restricted impedance controllers in physical human-robot interaction, conduct sensitivity analysis to identify the impact of control parameters on overall system stability, propose adaptive proxy parameter techniques to maximize transparency, and validate the results through simulation and experiments.
On the Design of Fast-Response Variable-Stiffness Continuum Robot with Electro-permanent Magnet-based Ball Joints
Taerim Lee, Donghyun Hwang
Robotic Intelligence
🎯 What it does: Designed a fast-response variable stiffness continuum robot based on electromagnetic permanent magnets (EPM) spherical joints, and verified its performance through experiments.
On the Design, Analysis, and Experimental Validation of Pneumatically-Actuated, Soft Robotic, Telescopic Structures
Bryan Busby, Minas V. Liarokapis
Robotic Intelligence
🎯 What it does: Studied the design parameters of soft mechanical expandable structures and their effects on expansion, speed, and smoothness, and experimentally validated these relationships.
On the Role of Jacobians in Robust Manipulation
Joshua T. Grace, A. Dollar
Robotic Intelligence
🎯 What it does: Proposed a new inverse Jacobian estimation method and a precise manipulation control method without prior knowledge, verified on the Yale Model O hand, Yale Stewart hand, and UR5e arm.
On the Vulnerability of LLM/VLM-Controlled Robotics
Xiyang Wu, A. S. Bedi
Robotic IntelligenceLarge Language ModelVision Language Model
🎯 What it does: Investigated the vulnerability of robot systems controlled by large language models (LLMs) and vision-language models (VLMs) under minor variations in input modalities, and proposed corresponding mathematical models and experimental validation methods.
On-Board Vision-Language Models (VLMs) for Personalized Motion Control of Autonomous Vehicles
Can Cui, Ziran Wang
Autonomous DrivingVision Language ModelRetrieval-Augmented Generation
🎯 What it does: Propose a lightweight in-vehicle vision-language model framework to achieve low-latency personalized driving control.
On-Chip Dynamic Mechanical Characterization: from Cells to Nucleus*
Jingjin Ge, Xiaoming Liu
VideoBiomedical Data
🎯 What it does: Propose a chip-based dynamic mechanical characterization method using microchannels, achieving automated measurement of mechanical properties of cancer and normal cells through high-frequency imaging and computer vision, revealing that nuclear mechanical properties determine overall cellular mechanical behavior.
On-Demand Motion Conversion of Magnetic Helical Microrobots Using Chemistry- and Microstructural-Modified Surface Wettability Modulation
Yaozhen Hou, Huaping Wang
Robotic IntelligencePhysics Related
🎯 What it does: Achieving on-demand motion conversion of magnetic helical microrobots in different liquid environments through surface chemistry and microstructure modification.
One-Shot Affordance Grounding of Deformable Objects in Egocentric Organizing Scenes
Wanjun Jia, Zhiyong Li
RecognitionObject DetectionMeta LearningPrompt EngineeringImage
🎯 What it does: Propose a one-shot deformable object localization method (OS-AGDO) that identifies unknown deformable objects in egocentric scenes with minimal samples.
One-Shot Gesture Recognition for Underwater Diver-To-Robot Communication*
Rishikesh Joshi, Junaed Sattar
RecognitionRobotic IntelligenceImageVideo
🎯 What it does: Proposed a single-demonstration-based underwater diver-robot mirror gesture recognition method.
One-shot Global Localization through Semantic Distribution Feature Retrieval and Semantic Topological Histogram Registration
Feixuan Huang, Heng Zhao
Pose EstimationAutonomous DrivingSimultaneous Localization and MappingPoint Cloud
🎯 What it does: Proposed a single-shot global localization method based on LiDAR semantic maps
One-Shot Robust Imitation Learning for Long-Horizon Visuomotor Tasks from Unsegmented Demonstrations
Shaokang Wu, Yanlong Huang
Robotic IntelligenceMeta LearningVideo
🎯 What it does: Proposes the MiLa framework, which utilizes dynamic motion primitives and meta-learning to achieve segmentation-free learning for long-horizon visual motion tasks, enabling rapid adaptation to unseen tasks through a single demonstration; simultaneously, the method demonstrates resilience to external disturbances and visual occlusions.
Online 6DoF Global Localisation in Forests using Semantically-Guided Re-Localisation and Cross-View Factor-Graph Optimisation
Lucas Carvalho de Lima, Milad Ramezani
Pose EstimationConvolutional Neural NetworkSimultaneous Localization and MappingImage
🎯 What it does: Propose the FGLoc6D method to achieve 6DoF global localization and online pose estimation for ground robots in forest environments with weakened GPS, utilizing deep semantic-guided relocalization and cross-view factor graph optimization.
Online Anti-Swing Trajectory Refinement for Variable-Length Cable-Suspended Aerial Transportation Robot
Haixin Yu, Xiao Liang
OptimizationRobotic Intelligence
🎯 What it does: Proposed an online trajectory refinement method for a drone transportation robot with variable-length cable suspension, achieving real-time reference trajectory optimization to suppress payload swinging.
Online Concurrent Multi-Robot Coverage Path Planning
Ratijit Mitra, Indranil Saha
OptimizationRobotic IntelligenceBenchmark
🎯 What it does: Proposed a centralized online multi-robot coverage path planning algorithm not based on a field-of-view window, supporting simultaneous planning and execution
Online Estimation of Table-Top Grown Strawberry Mass in Field Conditions with Occlusions
Jinshan Zhen, Ya Xiong
RestorationSegmentationDepth EstimationConvolutional Neural NetworkGenerative Adversarial NetworkImagePoint CloudAgriculture Related
🎯 What it does: Proposes a visual pipeline based on RGB-D and deep learning for online real-time estimation of desktop-planted strawberry mass, combining instance segmentation, occlusion completion, and tilt angle correction, then mapping geometric features to mass using polynomial regression.
Online Hierarchical Policy Learning using Physics Priors for Robot Navigation in Unknown Environments
Wei Chen, A. H. Qureshi
Robotic IntelligenceReinforcement LearningPhysics Related
🎯 What it does: Propose a hierarchical policy learning based on physical priors for robot navigation in unknown indoor environments.
Online Imitation Learning for Manipulation via Decaying Relative Correction through Teleoperation
Cheng Pan, Josie Hughes
Robotic IntelligenceReinforcement LearningAgriculture Related
🎯 What it does: A 6-degree-of-freedom (6-DOF) spatial correction system based on cable-driven remote operation is proposed, along with the introduction of a Decaying Relative Correction (DRC) method. This method temporarily applies spatial offset vectors provided by experts to policy trajectories, reducing the number of expert interventions and improving the success rate of robotic manipulators in operational tasks such as strawberry picking and cloth wiping through an online imitation learning framework.
Online Iterative Learning with Forward Simulation for Sub-minimum End-effector Displacement Positioning
Weiming Qu, Dingsheng Luo
Robotic Intelligence
🎯 What it does: Propose a novel high-precision robotic arm operation framework based on online iterative learning and forward simulation, capable of achieving sub-minimal end-effector displacement positioning.
Online Motion Planning for Quadrotor Multi-Point Navigation Using Efficient Imitation Learning-Based Strategy
Jin Zhou, Shuo Li
OptimizationRobotic IntelligenceSequential
🎯 What it does: Propose an online scheme based on imitation learning to enable quadrotor drones to achieve near real-time optimal trajectory navigation among multiple target points.
Online Residual Model Learning for Model Predictive Control of Autonomous Surface Vehicles in Real-World Environments
Arturo Gamboa-Gonzalez, Wei Wang
Autonomous DrivingOptimization
🎯 What it does: Proposed an online residual model learning framework that integrates offline simulation learning with real-time online learning into nonlinear model predictive control (MPC) for autonomous surface vehicle control.
Online Synthesis of Control Barrier Functions with Local Occupancy Grid Maps for Safe Navigation in Unknown Environments
Yuepeng Zhang, Xiang Yin
Autonomous Driving
🎯 What it does: In unknown environments, real-time online synthesis of control barrier functions (CBF) from local occupancy grid maps (OGM).
Online-HMM with Two-Layer Bayesian Method for Operator's Expected Speed Estimation in Teleoperated Gluing Tasks *
Wenke Zhou, Yu Wang
Computational EfficiencyRobotic Intelligence
🎯 What it does: Developed an online HMM combined with a two-layer Bayesian method to suppress operator's physiological tremors and estimate their desired speed.
OpAC: An Optimization-Augmented Control Framework for Single and Coordinated Multi-Arm Robotic Manipulation
Melih Özcan, Ozgur S. Oguz
OptimizationRobotic Intelligence
🎯 What it does: Proposed a multi-modal control framework OpAC, combining force control with optimization-enhanced motion planning to sequentially complete single-arm and multi-arm robotic manipulation tasks.
Open-loop Deep Reinforcement Learning Control of Soft Robotic In-hand Manipulations
Gabriel Suske, Oliver Sawodny
Robotic IntelligenceReinforcement Learning
🎯 What it does: Designed and implemented a novel anthropomorphic soft gripper, utilizing deep reinforcement learning to synthesize an open-loop controller, achieving the movement and manipulation of objects within the hand.
Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning
Rohit Mohan, Abhinav Valada
SegmentationAutonomous DrivingContrastive LearningPoint Cloud
🎯 What it does: Proposes a uncertainty-guided open-set LiDAR panoptic segmentation framework called ULOPS, which models prediction uncertainty using Dirichlet evidence learning and identifies and segments unknown instances during inference through uncertainty.
Open-World Task Planning for Humanoid Bimanual Dexterous Manipulation via Vision-Language Models
Zixin Tang, Fei Chen
Robotic IntelligenceVision Language ModelMultimodalityBenchmark
🎯 What it does: Proposes a large-scale benchmark called OBiMan-Bench for evaluating open-world task planning in dual-arm dexterous manipulation, and designs a zero-shot planning framework called OBiMan-Planner based on vision-language models, which includes two modules: scenario normalization and task planning;
OpenFusion++: An Open-vocabulary Real-time Scene Understanding System
Xiaofeng Jin, Matteo Matteucci
Object DetectionSegmentationDepth EstimationPoint CloudMesh
🎯 What it does: Proposes OpenFusion++, a real-time 3D semantic geometric reconstruction system based on TSDF for open-vocabulary scene understanding.
OpenGS-Fusion: Open-Vocabulary Dense Mapping with Hybrid 3D Gaussian Splatting for Refined Object-Level Understanding
Dianyi Yang, Yi Yang
SegmentationVision Language ModelGaussian Splatting
🎯 What it does: Proposed OpenGS-Fusion, an open-vocabulary dense mapping framework that enhances semantic modeling and refines 3D object-level understanding.
OpenMIGS: Multi-granularity Information-preserving Open-Vocabulary 3D Gaussian Splatting
Jingyu Zhao, Yufeng Yue
GenerationRetrievalGaussian SplattingPoint Cloud
🎯 What it does: Proposes the OpenMIGS framework, achieving multi-granularity, information-preserving open-vocabulary 3D Gaussian rendering through constructing object-level Gaussian fields and lightweight implicit fields.
OpenNav: Open-World Navigation with Multimodal Large Language Models
Mingfeng Yuan, Steven L. Waslander
Autonomous DrivingRobotic IntelligenceTransformerLarge Language ModelVision Language ModelVision-Language-Action ModelMultimodality
🎯 What it does: Propose a zero-shot visual-language navigation framework that leverages a multi-modal large language model (MLLM) to parse free-text instructions and generate top-down 2D value maps to synthesize trajectory points, enabling diverse navigation tasks with open-set instructions and objects.
OpenObject-NAV: Open-Vocabulary Object-Oriented Navigation Based on Dynamic Carrier-Relationship Scene Graph
Yujie Tang, Yufeng Yue
Autonomous DrivingRobotic IntelligenceGraph Neural NetworkLarge Language ModelReinforcement LearningVision-Language-Action Model
🎯 What it does: Propose an object navigation method based on open-vocabulary Carrier-Relationship Scene Graph (CRSG), and validate its effectiveness in the Habitat simulator and on real robots.
OpenRoboCare: A Multimodal Multi-Task Expert Demonstration Dataset for Robot Caregiving
Xiaoyu Liang, T. Bhattacharjee
Pose EstimationRobotic IntelligenceMultimodalityBenchmark
🎯 What it does: Collected the multimodal dataset OpenRoboCare, recording expert demonstrations by 21 occupational therapists performing 15 daily living activities on two mannequins.
OpenVox: Real-time Instance-level Open-vocabulary Probabilistic Voxel Representation
Yinan Deng, Yufeng Yue
Object DetectionSegmentationComputational EfficiencyRepresentation LearningVision Language ModelSimultaneous Localization and MappingImageText
🎯 What it does: Proposes OpenVox, a real-time incremental open-vocabulary probabilistic instance voxel representation, with a frontend employing efficient instance segmentation and a language reasoning pipeline based on title encoding, and a backend implementing probabilistic instance voxels and cross-frame incremental fusion, including instance association and real-time map evolution.
Opportunistic Collaborative Planning with Large Vision Model Guided Control and Joint Query-Service Optimization
Jiayi Chen, Chengzhong Xu
Autonomous DrivingOptimization
🎯 What it does: Proposes an opportunistic collaborative planning (OCP) method that integrates an efficient local model with a cloud-based large visual model to enable autonomous vehicle navigation in open scenarios.
Opt-in Camera: Person Identification in Video via UWB Localization and Its Application to Opt-in Systems
Matthew Ishige, Ryo Yonetani
RecognitionSafty and PrivacySimultaneous Localization and MappingVideo
🎯 What it does: Developed an opt-in camera system that utilizes UWB tag tracking and visual identification to record only individuals who have explicitly consented to being recorded
Opti-Acoustic Scene Reconstruction in Highly Turbid Underwater Environments
Ivana Collado-Gonzalez, Brendan Englot
Convolutional Neural NetworkImageMultimodalityAudio
🎯 What it does: A real-time photoacoustic scene reconstruction method is proposed, specifically optimized for turbid water environments, utilizing image region of interest (ROI) matching with sonar data, and combining sonar distance and camera height information for reconstruction.
OptiGrasp: Optimized Grasp Pose Detection Using RGB Images for Warehouse Picking Robots
Simranjeet Singh, Joshua R. Smith
Pose EstimationRobotic IntelligenceConvolutional Neural NetworkSupervised Fine-TuningImage
🎯 What it does: Achieve suction grasping pose detection using only RGB images and foundation models, achieving an 81.9% success rate on a real robot
Optimal Control of Walkers with Parallel Actuation
Ludovic De Matteïs, Nicolas Mansard
OptimizationRobotic Intelligence
🎯 What it does: Achieving more precise generation of walking motion by explicitly considering the kinematic constraints of the closed-loop chain in optimal control problems.
Optimal Motion Scaling for Delayed Telesurgery
Jason Lim, Michael C. Yip
OptimizationRobotic Intelligence
🎯 What it does: Conduct user experiments to investigate the relationship between network latency, motion scaling factors, and teleoperation surgery performance, and propose a personalized scaling factor mapping model
Optimal Scheduling of a Dual-Arm Robot for Efficient Strawberry Harvesting in Plant Factories
Yuankai Zhu, Chen Peng
OptimizationAgriculture Related
🎯 What it does: Propose a Mixed Integer Linear Programming (MILP) framework for systematic scheduling and coordination of dual-arm harvesting robots, minimizing the total harvesting completion time by utilizing pre-mapped fruit positions.
Optimal Trajectory Planning in a Vertically Undulating Snake Locomotion using Contact-implicit Optimization
Adarsh Salagame, Alireza Ramezani
OptimizationRobotic IntelligenceOrdinary Differential Equation
🎯 What it does: Proposed a simplified model based on Moreau's stepping forward method, and validated its effectiveness in vertical swinging snake robot motion planning through simulation and experiments.
Optimization Based Human-Guided Variable-Stiffness Visual Impedance Control for Contact-Rich Tasks
Jiao Jiang, Hui Zhang
OptimizationRobotic IntelligenceImage
🎯 What it does: Proposed a human-guided visual impedance control framework for contact-rich tasks (such as polishing and drilling), and developed a variable stiffness visual impedance control strategy that uses online quadratic programming optimization to adjust impedance parameters, enabling the tool contact force to converge to the desired value while satisfying safety constraints.
Optimization-Based Path-Velocity Control for Time-Optimal Path Tracking under Uncertainties
Zheng Jia, Björn Olofsson
Autonomous DrivingOptimization
🎯 What it does: Proposed a real-time prediction scaling algorithm to achieve time-optimal path tracking under model uncertainty.
Optimized Optical Fiber Sensors for Forearm Muscle Deformation Monitoring and Hand Motion Recognition
Heifu Liu, Wei Meng
RecognitionPose EstimationTransformerBiomedical Data
🎯 What it does: Designed and optimized a flexible distributed fiber Bragg grating (FBG) sensor for monitoring forearm muscle deformation and hand motion recognition, and experimentally validated its performance.
Optimizing for Ride Comfort: A Model Predictive Control Framework with Frequency-Domain Analysis of the Acceleration Sequence
Chun-Chien Hsiao, Alireza Talebpour
Autonomous DrivingOptimizationSequential
🎯 What it does: Proposes an MPC framework centered on ride comfort, optimizing tangential and lateral acceleration patterns in 2D maneuvers, including longitudinal acceleration and steering rate.
Optoelectronic Navigation-Based Microtruck: For Efficient Cargo Loading, Transport, and Unloading
Ao Wang, Lin Feng
OptimizationPhysics Related
🎯 What it does: Propose a photonic navigation microcar based on Ag-SiO₂ microspheres, achieving efficient adsorption, rapid transportation, and targeted unloading of negatively charged dielectric particles by adjusting electric field frequency and optical parameters.
ORA-NET: Enhancing Image Feature Matching through Oriented Overlapping Region Alignment
Te Cui, Yufeng Yue
RetrievalImage
🎯 What it does: Proposes ORA-NET to enhance image feature matching performance through directional overlapping region alignment
ORBiT: Optimizing Robot-Assisted Bite Transfer Leveraging a Real2Sim2Real Framework
Sherwin Stephen Chan, Wei Tech Ang
OptimizationRobotic Intelligence
🎯 What it does: Developed the ORBiT framework, which optimizes bite transfer during robot-assisted feeding using motion capture-driven high-fidelity soft-body simulation, identifies transfer parameters that minimize contact force through systematic parameter search, and finally validates the approach on a real robot system.
ORCA: An Open-Source, Reliable, Cost-Effective, Anthropomorphic Robotic Hand for Uninterrupted Dexterous Task Learning
Clemens C. Christoph, Robert K. Katzschmann
Robotic IntelligenceReinforcement Learning
🎯 What it does: Developed and publicly released a 17-degree-of-freedom (DOF) tendon-driven humanoid robot hand ORCA, which can be assembled within 8 hours at a low cost (<2000 CHF), and demonstrated its applications in teleoperation, imitation learning, and zero-shot sim-to-real reinforcement learning tasks.
OrchardDepth++: Binned KL-Flood Regularization for Monocular Depth Estimation of Orchard Scene
Zhichao Zheng, Bruce A. MacDonald
Depth EstimationSupervised Fine-TuningImageAgriculture Related
🎯 What it does: Proposed and implemented a monocular depth estimation method based on binned KL-Flood regularization, specifically designed for orchard scenarios.
Origami-Inspired Pneumatic Continuum Manipulator: Stiffness Modeling and Validation
Zhuowen Li, Fan Xu
Robotic Intelligence
🎯 What it does: Developed a stiffness model for the schematic pneumatic continuum manipulator (OPM) and experimentally validated its predictive accuracy;
Origami-Inspired Soft Gripper with Tunable Constant Force Output
Zhenwei Ni, Cecilia Laschi
Robotic Intelligence
🎯 What it does: Proposed a flexible gripper based on origami principles that can achieve adjustable constant grip force within a wide deformation range;
Oscillation Suppression of Acoustic Trapping: A Disturbance Observer-based Approach
Yuyu Jia, Song Liu
OptimizationImagePhysics Related
🎯 What it does: Propose a control method based on visual feedback, which uses a binocular microscope vision system to locate particles and dynamically adjusts the sound field distribution through a disturbance observer, thereby suppressing the oscillation of the traveling-wave acoustic comb micro-sector laser along the z-axis.
OSMa-Bench: Evaluating Open Semantic Mapping Under Varying Lighting Conditions
M. Popov, S. Kolyubin
SegmentationVision Language ModelSimultaneous Localization and MappingVideoPoint CloudBenchmark
🎯 What it does: Evaluated the performance of open-source semantic mapping (OSM) under different indoor lighting conditions; proposed a dynamic configurable, automated LLM/LVLM-driven evaluation pipeline called OSMa-Bench; and introduced a new simulated RGB-D sequence dataset along with corresponding 3D reconstructed ground truth data for rigorous performance analysis.
Osmosis-Driven Large-Scale Actuation for Shape-Shifting Mechanisms
Elio J. Challita, Robert J. Wood
Robotic IntelligencePhysics Related
🎯 What it does: Achieving large-scale, reversible deformation-driven actuation using superabsorbent polymer (SAP) particles, demonstrating expansion-contraction motion generating approximately 10 N force through water absorption swelling, and integrating it into a deformable wheel to enable adaptive locomotion on land and in water.
Out-of-Distribution Recovery with Object-Centric Keypoint Inverse Policy for Visuomotor Imitation Learning
George Jiayuan Gao, Nadia Figueroa
Robotic Intelligence
🎯 What it does: Proposed an object keypoint-based recovery framework OCR to address OOD scenarios in visual kinematic policy learning.
Overlap-Aware Feature Learning for Robust Unsupervised Domain Adaptation for 3D Semantic Segmentation
Junjie Chen, Kemi Ding
SegmentationDomain AdaptationAdversarial AttackContrastive LearningPoint Cloud
🎯 What it does: Proposes a three-component framework, including a robustness evaluation model, reversible attention alignment module, and quality-guided contrastive memory bank, to address the issues of feature overlap and structural erosion in 3D point cloud semantic segmentation under unsupervised domain adaptation, while enhancing model robustness against adversarial attacks.
OVSG-SLAM: Open-Vocabulary Semantic Gaussian Splatting SLAM
Zhehang Liu, Yuwei Wu
Autonomous DrivingComputational EfficiencyVision Language ModelGaussian SplattingSimultaneous Localization and MappingImagePoint Cloud
🎯 What it does: Propose an open-vocabulary semantic SLAM framework OVSG-SLAM that combines multimodal perception with 3D Gaussian diffusion
P2 Explore: Efficient Exploration in Unknown Cluttered Environment with Floor Plan Prediction
Kun Song, Mingyu Ding
Autonomous DrivingOptimizationRobotic IntelligenceSimultaneous Localization and MappingImage
🎯 What it does: Propose FPUNet for efficiently predicting layouts in noisy indoor environments, extracting room segmentation, and constructing their topological connectivity, while optimizing the access order of predicted rooms to provide high-level exploration guidance;
PACR: Point-Axis Constraint Reasoning for Enhanced Robotic Manipulation with Dexterity and Compliance
Haowen Xiong, Jianxing Liu
OptimizationRobotic IntelligenceImagePoint CloudChain-of-Thought
🎯 What it does: Proposed the PACR framework, which jointly optimizes robot trajectories and damping curves, and employs a dual-agent Vision-Language Model for constraint generation and validation as well as an error backtracking mechanism;
PainDiffusion: Learning to Express Pain
Quang Tien Dam, Joo-Ho Lee
GenerationDiffusion modelVideoBiomedical Data
🎯 What it does: Proposed PainDiffusion, a diffusion model that generates natural pain expressions;
PANDAS: Prediction and Detection of Accurate Slippage
Teng Yan, Yang Zhang
Anomaly DetectionMultimodalityPhysics Related
🎯 What it does: Propose the PANDAS framework, integrating physics-informed multimodal spatiotemporal networks with probabilistic temporal reasoning modules for sliding detection and prediction.
PanopticSplatting: End-to-End Panoptic Gaussian Splatting
Yuxuan Xie, Yue Wang
SegmentationGaussian SplattingPoint Cloud
🎯 What it does: Proposed an end-to-end open-vocabulary semantic panoptic Gaussian splatting system (PanopticSplatting), achieving 2D instance mask cross-frame association-free upsampling, and enhancing 3D segmentation consistency through label mixing and label warping.
Parallel Transmission Aware Co-Design: Enhancing Manipulator Performance Through Actuation-Space Optimization
Rohit Kumar, Frank Kirchner
OptimizationRobotic Intelligence
🎯 What it does: Propose a collaborative design method for parallel mechanisms, optimizing the gear ratio and performing trajectory optimization in the execution space.
Parameter Selections and Applications for Soft Bellows Actuators (SBAs) with Various Performance Metrics
Wenjing Zou, Chao Wang
Optimization
🎯 What it does: This paper systematically investigates the effects of six key parameters on the load capacity, displacement efficiency, and bending resistance of soft pneumatic actuators (SBAs), and based on the findings, designs task-specific SBAs for applications such as high-load pneumatic grippers, high-efficiency displacement stages, and insect-like crawling robots.
Parameterized Motion Planning for Aerial Manipulators in Contact with Unstructured Surfaces
Zhixing Zhang, Yaonan Wang
OptimizationRobotic Intelligence
🎯 What it does: Proposed a sampling-based motion planning method called PCS-FMT* for continuous contact motion planning of drone manipulators on complex unstructured surfaces.
ParkDiffusion: Heterogeneous Multi-Agent Multi-Modal Trajectory Prediction for Automated Parking using Diffusion Models
Jiarong Wei, Abhinav Valada
Autonomous DrivingGraph Neural NetworkDiffusion modelMultimodality
🎯 What it does: Proposed the ParkDiffusion model for automatic parking, achieving prediction of vehicle and pedestrian trajectories
PartGrasp: Generalizable Part-level Grasping via Semantic-Geometric Alignment
Haoyang Lu, Yufeng Yue
Pose EstimationRobotic Intelligence
🎯 What it does: Proposed the PartGrasp method, achieving general and precise part-level grasping through semantic-geometric hierarchical alignment.
Partial Feedback Linearization Control of a Cable-Suspended Multirotor Platform for Stabilization of an Attached Load
Hemjyoti Das, Christian Ott
Robotic IntelligencePhysics Related
🎯 What it does: Propose a control method based on partial feedback linearization (PFL) for the stabilization control of suspended aerial platforms and their additional loads.
PathCluster: Pedestrian Group-Adaptive Social Navigation in Dense Crowds
Nihal Gunukula, Aniket Bera
Robotic IntelligenceSequential
🎯 What it does: Propose the PathCluster method, which improves social navigation for mobile robots in extremely crowded environments through group generation based on similar trajectories.
Pathfinder for Low-altitude Aircraft with Binary Neural Network
Kaijie Yin, Hui Kong
GenerationAutonomous DrivingConvolutional Neural NetworkTransformerImageMultimodalityPoint Cloud
🎯 What it does: Proposes an OSM generation method utilizing low-altitude aircraft equipped with aerial sensors, with the core being a binary dual-stream road segmentation model based on LiDAR and camera data to achieve efficient path finding and generate complete OSM maps.
PAVLM: Advancing Point Cloud based Affordance Understanding Via Vision-Language Model
Shang-Ching Liu, Jianwei Zhang
RecognitionTransformerPrompt EngineeringVision Language ModelPoint CloudBenchmark
🎯 What it does: By combining a geometry-guided propagation module with the hidden embeddings of a Large Language Model (LLM), the Vision-Language Model is utilized to enhance manipulability understanding of point clouds.
PB-MOT: Pose-aware Association Boosted Online 3D Multi-Object Tracking
Bo Pang, Liang Li
Object TrackingPose EstimationAutonomous DrivingPoint CloudBenchmark
🎯 What it does: Proposes the PB-MOT online 3D multi-target tracking framework, integrating pose-aware association to enhance tracking performance.
PC-SRIF: Preconditioned Cholesky-based Square Root Information Filter for Vision-aided Inertial Navigation
Tong Ke, Ryan C. DuToit
Autonomous DrivingOptimizationSimultaneous Localization and Mapping
🎯 What it does: Proposed a preconditioned Cholesky-based square root information filter (PC-SRIF) for solving linear systems in visual-inertial navigation systems (VINS).
PC2P: Multi-Agent Path Finding via Personalized-Enhanced Communication and Crowd Perception
Guotao Li, Yuhui Sun
OptimizationReinforcement Learning
🎯 What it does: Propose PC2P, a multi-agent path planning method combining Q-learning, utilizing personalized enhanced communication, congestion awareness, and regional deadlock-breaking strategies
PCGE: Boosting 3D Visual Grounding via Progressive Comprehension and Geometric-topology Perception Enhancement
Zeyu Wang, Jiamao Li
Pose Estimation
🎯 What it does: Proposes the PCGE framework, decomposing the 3D visual localization task into two steps: keypoint estimation and size regression.
PCMF2-Net: A Pyramid Cross-Modal Feature Fusion Network for Off-Road Freespace Detection
Ming Gao, Hui Kong
SegmentationAutonomous DrivingConvolutional Neural NetworkTransformerImageMultimodality
🎯 What it does: Proposes a pyramid cross-modal feature fusion network called PCMF2-Net for off-road airspace detection. The network takes dense depth maps, RGB images, and surface normal maps as input, employs a dual-branch CNN-Transformer encoder to extract local and global features, and uses a pyramid cross-modal feature fusion module for multi-scale, multi-modal feature fusion. It also incorporates an edge segmentation task and a two-step training strategy to enhance performance.
PD-VLA: Accelerating Vision-Language-Action Model Integrated with Action Chunking via Parallel Decoding
Wenxuan Song, Haoang Li
Computational EfficiencyRobotic IntelligenceVision-Language-Action Model
🎯 What it does: Proposed the PD-VLA framework, which accelerates the integration of Vision-Language-Action (VLA) models with action chunking through parallel decoding technology, thereby improving robotic manipulation efficiency.
PEACE: Prompt Engineering Automation for CLIPSeg Enhancement for Safe-Landing Zone Segmentation
Haechan Mark Bong, Giovanni Beltrame
SegmentationTransformerPrompt EngineeringVision Language ModelImage
🎯 What it does: Developed the PEACE system to automatically generate and refine CLIPSeg prompts for identifying safe landing zones in changing environments.
Peg-in-hole assembly method based on visual reinforcement learning and tactile pose estimation
Yong Tao, Hongxing Wei
Pose EstimationRobotic IntelligenceReinforcement LearningMultimodality
🎯 What it does: Proposes a pre-assembly method based on visual reinforcement learning combined with tactile pose estimation for real-time adjustment to improve the success rate of slot assembly.
Perception-aware Planning for Quadrotor Flight in Unknown and Feature-limited Environments
Chenxi Yu, Boyu Zhou
OptimizationRobotic IntelligenceSimultaneous Localization and Mapping
🎯 What it does: Proposes a perception-driven planning method for quadrotors in unknown, feature-scarce environments, incorporating perspective conversion graphs and yaw trajectory generation that simultaneously balance exploration and localizability;
Performance consequences of information-based centralization arising from neural and mechanical coupling in a walking robot
Ellen Liu, P. Manoonpong
Robotic Intelligence
🎯 What it does: By adjusting joint stiffness and leg controller coupling, independently altering mechanical coupling and neural coupling to evaluate the degree of centralization in hexapod robots.
Perpetua: Multi-Hypothesis Persistence Modeling for Semi-Static Environments
Miguel A. Saavedra-Ruiz, Liam Paull
Object Tracking
🎯 What it does: Designed and implemented the Perpetua method to model the persistence and emergence probabilities of features in a semi-static environment, supporting multi-hypothesis tracking and predicting future feature states.
Persistent Preservation of a Spatio-temporal Environment Under Uncertainty
Amel Nestor Docena, Alberto Quattrini Li
OptimizationRobotic IntelligenceReinforcement Learning
🎯 What it does: Studies how a single robot can continuously plan visit times and charge to maintain the spatiotemporal properties of regions of interest in the environment under state uncertainty;
Personalized Re-identification through Unsupervised Continual Learning and Parallel Training
Federico Rollo, Navvab Kashiri
RecognitionImage
🎯 What it does: Propose a method to enhance and continuously adapt re-identification for specific targets through unsupervised continual learning and intelligent image pool collection, personalizing neural networks.
Personalized Reinforcement Learning Control of Soft Robotic Exosuit for Assisting Human Normative Walking with Reduced Effort
Emiliano Quiñones Yumbla, Wenlong Zhang
Robotic IntelligenceSupervised Fine-TuningReinforcement LearningBiomedical Data
🎯 What it does: Propose an offline learning soft exoskeleton controller and online tuning to personalize assistance for human normal gait.
Personalized Robotic Achilles Tendon Utilizing a Semi-Passive Spring with Switching Stiffness*
Mingyu Seong, Jungsu Choi
Robotic Intelligence
🎯 What it does: Developed a lightweight semi-passive spring ankle assistive device called the robotic Achilles tendon (RAT), achieving elastic support during gait phases via double-acting cylinders and electromagnetic valves;
Pet-NODE Modeling: Embedding Priors and Time-Series Features into Neural ODE
Jia Chen, Shihua Li
Robotic IntelligenceTime SeriesOrdinary Differential Equation
🎯 What it does: Propose the Pet-NODE framework, which integrates physical priors and temporal features into a neural ordinary differential equation (NODE) model for high-fidelity modeling of mobile robot dynamic systems; and further embeds domain knowledge through a self-predictive target loss function.
PhyGrasp: Generalizing Robotic Grasping with Physics-informed Large Multimodal Models
Dingkun Guo, Wei Zhan
Robotic IntelligenceTransformerLarge Language ModelVision-Language-Action ModelTextMultimodalityPoint CloudPhysics Related
🎯 What it does: Proposed a physics-informed large-scale multimodal model called PhyGrasp, which integrates natural language and 3D point clouds through a bridging module to enhance robotic grasping performance; simultaneously constructed the PhyPartNet dataset containing 195K object instances and their linguistic descriptions, and conducted experimental evaluations on both simulated and real robots.
PhysGCN-DL: Physics-Informed Graph Convolutional Networks with Diversity-Aware Loss Optimization for Multimodal Pedestrian Trajectory Prediction
Zihan Jiang, Weidong Zhang
OptimizationGraph Neural NetworkMultimodalityPhysics Related
🎯 What it does: Proposed a physics-informed graph convolutional network (PhysGCN-DL), which models pedestrian dynamic interactions by representing physical interactions between pedestrians as edge weights in graph convolutions, thereby enhancing the diversity and accuracy of trajectory prediction.
Physical Human-Robot Collaboration-Assisted Acetabular Preparation for Total Hip Replacement Surgery
Ziqi Wang, Shoudong Huang
Robotic Intelligence
🎯 What it does: Using collaborative robots with variable impedance control for acetabular preparation in hip replacement surgery;
Physically-Feasible Reactive Synthesis for Terrain-Adaptive Locomotion via Trajectory Optimization and Symbolic Repair
Ziyi Zhou, Ye Zhao
OptimizationRobotic IntelligencePhysics Related
🎯 What it does: Propose an integrated planning framework for quadruped robots to achieve feasible ground gaits on dynamic, unpredictable terrains; the framework combines symbolic reactive synthesis with mixed-integer convex programming (MICP) for dynamic and physically feasible motion planning, and enhances the scalability of synthesis through symbolic repair and a high-level manager.