ICRA 2025 Papers — Page 8
IEEE International Conference on Robotics and Automation · 1604 papers
ICRT: In-Context Imitation Learning via Next-Token Prediction
Letian Fu, Kenneth Y. Goldberg
Robotic IntelligenceTransformerSequential
🎯 What it does: Propose ICRT, a causal transformer that uses autoregressive prediction for sensory motion trajectories, enabling scenario imitation learning without training and allowing flexible task execution in new environments.
iKap: Kinematics-Aware Planning with Imperative Learning
Qihang Li, Chen Wang
OptimizationRobotic Intelligence
🎯 What it does: Proposed and implemented a visual-planning system called iKap, which directly integrates the robot's kinematic model into the learning pipeline, achieving self-supervised learning and learning collision-safe and kinematically feasible trajectories through differentiable bilevel optimization.
Illumination Adaptation for SAM to Achieve Accurate Segmentation of Images Taken in Low-Light Scenes
Hongmin Mu, Zhengcai Cao
SegmentationDomain AdaptationTransformerImage
🎯 What it does: An adaptive method for the Segment Anything Model (SAM) is proposed to address low-light scenarios, incorporating self-training, low-light feature enhancement head, and domain shift compensation loss to achieve more accurate image segmentation.
Image-Based Compliance Control for Robotic Steering of a Ferromagnetic Guidewire
An Hu, Yu Sun
Robotic IntelligenceImageBiomedical Data
🎯 What it does: This paper utilizes only 2D perspective images as feedback, proposing a model-based external force observer to perceive unknown interactions between the guidewire and vascular wall, and designs a compliant controller to achieve safe and stable guidance of magnetic guidewires;
Image-Guided Surgical Planning for Percutaneous Nephrolithotomy Using CTRs: A Phantom-Based Study
Filipe C. Pedrosa, J. Jayender
Robotic IntelligenceBiomedical DataComputed Tomography
🎯 What it does: The optimal planning algorithm based on patient-specific coaxial tube robots (CTRs) for PCNL surgery planning was validated on a realistic right thoracic model.
Imitation Learning with Limited Actions via Diffusion Planners and Deep Koopman Controllers
Jianxin Bi, Harold Soh
Representation LearningRobotic IntelligenceDiffusion modelSequential
🎯 What it does: Proposes a plan-then-control framework that learns latent action representations in a Deep Koopman dynamics model using observational demonstration data, and maps them to high-dimensional continuous actions through a linear action decoder, significantly reducing the demand for labeled action data.
IMOST: Incremental Memory Mechanism with Online Self-Supervision for Continual Traversability Learning
Kehui Ma, Ling Pei
Autonomous DrivingComputational EfficiencyRepresentation Learning
🎯 What it does: Designed and implemented the IMOST framework, leveraging incremental dynamic memory and online self-supervised annotation to achieve continuous traversability learning.
Impedance Primitive-Augmented Hierarchical Reinforcement Learning for Sequential Tasks
Amin Berjaoui Tahmaz, J. Kober
Robotic IntelligenceReinforcement Learning
🎯 What it does: Proposes a hierarchical reinforcement learning framework enhanced with impedance primitives, capable of efficiently performing mechanical operations in continuous contact tasks;
Implicit Articulated Robot Morphology Modeling with Configuration Space Neural Signed Distance Functions
Yiting Chen, A. Billard
Computational EfficiencyRobotic Intelligence
🎯 What it does: Proposes an implicit robot morphology modeling method based on the configuration space signature distance function (Robot Neural Distance Function, RNDF), utilizing forward kinematics to achieve precise encoding and optimize the computational efficiency and accuracy of distance queries.
Implicit Contact Diffuser: Sequential Contact Reasoning With Latent Point Cloud Diffusion
Zixuan Huang, Dmitry Berenson
Robotic IntelligenceDiffusion modelPoint Cloud
🎯 What it does: Propose Implicit Contact Diffuser (ICD), a diffusion-based model that generates a series of neural descriptors specifying the sequence of contact relationships between objects and the environment, and uses this sequence as guidance for Model Predictive Control (MPC) methods to achieve long-horizon manipulation tasks with rich contact interactions.
Implicit Physics-aware Policy for Dynamic Manipulation of Rigid Objects via Soft Body Tools
Zixing Wang, A. H. Qureshi
Robotic IntelligenceWorld ModelPhysics Related
🎯 What it does: Proposed and verified an implicit physical awareness (IPA) strategy for dynamically moving rigid objects using flexible tools (e.g., ropes) in unknown environmental physical parameters with a single attempt;
Impossibility of Self-Organized Aggregation Without Computation
Roy Steinberg, Kiril Solovey
Robotic Intelligence
🎯 What it does: Prove that in robot systems without computational capability, it is impossible to achieve aggregation of any number of robots with any controller, and propose an alternative controller along with a rigorous proof of aggregation.
Improved Bag-of-Words Image Retrieval with Geometric Constraints for Ground Texture Localization
Aaron Wilhelm, Nils Napp
RetrievalSimultaneous Localization and MappingImage
🎯 What it does: Proposed an improved bag-of-words (BoW) based image retrieval system for ground texture localization, achieving higher global localization accuracy and improved loop closure detection precision and recall in SLAM, with two versions providing high precision and high speed respectively
Improving Coverage Performance of a Size-Reconfigurable Robot Based on Overlapping and Reconfiguration Reduction Criteria
M. V. J. Muthugala, M. R. Elara
OptimizationRobotic Intelligence
🎯 What it does: Propose a size-reconfigurable robot coverage path planning method based on overlap and reconfiguration reduction criteria
Improving Efficiency in Path Planning: Tangent Line Decomposition Algorithm
Yu Tian, Hongliang Ren
Autonomous DrivingOptimization
🎯 What it does: Propose a tangent decomposition algorithm (TLD) for efficiently finding near-optimal collision-free paths in 2D and 3D environments
Improving Generalization Ability for 3D Object Detection by Learning Sparsity-Invariant Features
Hsin-Cheng Lu, Winston H. Hsu
Object DetectionDomain AdaptationKnowledge DistillationPoint Cloud
🎯 What it does: This paper proposes a 3D object detection method for a single source domain, aiming to enhance the model's generalization capability in target domains with different sensor configurations and scene distributions.
Improving Grip Stability Using Passive Compliant Microspine Arrays for Soft Robots in Unstructured Terrain
Lauren Ervin, V. Vikas
Robotic Intelligence
🎯 What it does: Designed and implemented a passive compliant micro-ridge stacked array for soft robot legs to enhance grasping and walking performance on irregular terrain.
Improving Indoor Localization Accuracy by Using an Efficient Implicit Neural Map Representation
Haofei Kuang, C. Stachniss
Pose EstimationRobotic IntelligenceSimultaneous Localization and MappingPoint Cloud
🎯 What it does: Propose an implicit neural map representation to capture position and orientation geometric features from 2D LiDAR scans, and combine a lightweight neural network with a traditional Monte Carlo localization framework to design an efficient observation model, achieving real-time robot pose estimation.
Improving Monocular Visual-Inertial Initialization with Structureless Visual-Inertial Bundle Adjustment
Junlin Song, M. Olivares-Méndez
Pose EstimationOptimizationSimultaneous Localization and MappingMultimodality
🎯 What it does: Propose a structureless visual-inertial bundle adjustment method to improve the initialization process of monocular visual-inertial odometry (VIO).
Improving Probe Localization for Freehand 3D Ultrasound Using Lightweight Cameras
Dianye Huang, Zhongliang Jiang
Pose EstimationDomain AdaptationConvolutional Neural NetworkAuto EncoderContrastive LearningImageBiomedical DataUltrasound
🎯 What it does: Propose a cost-effective and scalable method for handheld 3D ultrasound probe pose localization using two lightweight cameras.
Improving Vision-Language-Action Model with Online Reinforcement Learning
Yanjiang Guo, Jianyu Chen
Computational EfficiencySupervised Fine-TuningReinforcement LearningVision-Language-Action Model
🎯 What it does: Propose the iRe-VLA framework, which iteratively improves vision-language-action (VLA) models through alternating reinforcement learning and supervised learning.
Improving Zero-Shot ObjectNav with Generative Communication
Vishnu Sashank Dorbala, Dinesh Manocha
Robotic IntelligencePrompt EngineeringVision Language ModelMultimodality
🎯 What it does: Propose a method to enhance navigation performance in zero-shot ObjectNav by leveraging generative communication (GC), utilizing a vision-language model (VLM) for information exchange between a global-perspective upper auxiliary agent and a limited-perspective ground agent.
IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition
Rui Liu, Pratap Tokekar
Representation LearningRobotic IntelligenceMultimodality
🎯 What it does: Propose the IMRL method, integrating visual, physical, temporal, and geometric representations for imitation learning strategies in food acquisition, capturing food types, physical properties, action temporal dynamics, and geometric information to adapt scooping strategies under different scenarios.
In the Wild Ungraspable Object Picking with Bimanual Nonprehensile Manipulation
Albert Wu, Dan Kruse
Robotic IntelligenceImage
🎯 What it does: Utilizing dual-arm non-grasping operations to identify grasp points from visual information on compact shelves, gently pushing away obstacles when necessary, and subsequently using side-contact bimanual grasping to pick up objects that cannot be handled by traditional grippers.
In-Context Learning Enables Robot Action Prediction in LLMs
Yida Yin, Roei Herzig
Robotic IntelligenceTransformerLarge Language ModelPrompt EngineeringText
🎯 What it does: Propose the RoboPrompt framework, enabling text-based LLMs to directly predict robot actions through ICL without training.
In-Pipe Navigation Development Environment and a Smooth Path Planning Method on Pipeline Surface
Hao Liu, Mingquan Lu
OptimizationRobotic Intelligence
🎯 What it does: Proposed an open-source pipeline internal navigation development environment and designed a smooth path planning method based on the pipeline axis, subsequently verifying its usability in simulation and real environments.
In-Plane Manipulation of Soft Micro-Fiber with Ultrasonic Transducer Array and Microscope
Jie Zou, Song Liu
Robotic IntelligenceUltrasound
🎯 What it does: An automated ultrasonic manipulation system was constructed for non-contact manipulation of soft microfibers in-plane, along with the design of a real-time capture generation algorithm and theoretical analysis.
In-Vivo Cable-Driven Rodent Ankle Exoskeleton System for Sensorimotor Rehabilitation
Juwan Han, Keehoon Kim
Robotic IntelligenceBiomedical Data
🎯 What it does: Introduced a cable-driven ankle exoskeleton system for in vivo studies, experimentally evaluating its effects on gait and sensory-motor recovery under anesthetized and awake conditions.
Incremental Few-Shot Adaptation for Non-Prehensile Object Manipulation Using Parallelizable Physics Simulators
Fabian Baumeister, Joerg Stueckler
OptimizationRobotic IntelligenceMeta LearningPhysics Related
🎯 What it does: Proposed an incremental few-shot adaptation method that utilizes parallelizable rigid-body physics simulation and sampling optimization to calibrate the dynamics model for manipulating non-grasped objects.
Individual and Collective Behaviors in Soft Robot Worms Inspired by Living Worm Blobs
Carina Kaeser, Justin Werfel
Robotic Intelligence
🎯 What it does: Designed and demonstrated a set of pneumatic soft robots inspired by California black ants, studied their motion behavior and physical entanglement strength in individual and collective states, and compared them with live ants.
Indoor and Outdoor Multi-Terrain Stair-Climbing Robot Design
Wei-Ting Chen, Pei-Chun Lin
Robotic Intelligence
🎯 What it does: Proposed an indoor and outdoor multi-terrain stair-climbing robot named IOMT, and designed key functions such as four-wheel independent drive and steering, and control of stable pitch angle.
Indoor Localization of UAVs Using Only Few Measurements by Output-Sensitive Preimage Intersection
Michael M. Bilevich, Dan Halperin
Robotic IntelligenceSimultaneous Localization and Mapping
🎯 What it does: Propose a deterministic indoor UAV positioning method using only a small number of downward distance measurements and corresponding odometry, achieved through preimage intersection and spatial subdivision search.
Inducing Matrix Sparsity Bias for Improved Dynamic Identification of Parallel Kinematic Manipulators using Deep Learning
M. Lahoud, Ferdinando Cannella
Computational EfficiencyRobotic IntelligencePhysics Related
🎯 What it does: Proposed and validated a physics-informed neural network (PINN)-based dynamic model for the Delta parallel manipulator ABB IRB 360-6/1600, and introduced a sparsity constraint on the mass matrix;
Inference Based Multi-Object Reactive Search in a Partially Known Environment With Temporal Logic Specifications
Yaohui Kang, Zheng Kan
Robotic IntelligenceLarge Language Model
🎯 What it does: Proposes a reasoning-based multi-object reactive search framework that infers co-occurrence values between target objects and known landmarks using the COMET model, integrating the inferred results into LTL-constrained reactive temporal logic motion planning to enable dynamic multi-object search by robots in partially known environments.
Inference-Time Policy Steering Through Human Interactions
Yanwei Wang, Julie Shah
Reinforcement Learning from Human FeedbackDiffusion model
🎯 What it does: Propose an ITPS framework that guides generative strategies through human interaction during the inference phase.
Infield Self-Calibration of Intrinsic Parameters for Two Rigidly Connected IMUs
Can Huang, Kejian J. Wu
OptimizationRobotic Intelligence
🎯 What it does: Studied the intrinsic parameter self-calibration of two rigidly connected IMUs using only IMU data and known extrinsic parameters, focusing on observability analysis. It proved that gyroscope intrinsic parameters and partial accelerometer biases are observable, while identifying unobservable directions caused by degenerate motions. Numerical simulations verified the observability, and real data were used to evaluate calibration accuracy.
Informed Repurposing of Quadruped Legs for New Tasks
Fuchen Chen, Daniel M. Aukes
Robotic Intelligence
🎯 What it does: Studies how to evaluate and repurpose existing quadruped legs for new tasks, implementing the method on 15 robot designs generated from six preselected leg design combinations.
InsCMPR: Efficient Cross-Modal Place Recognition via Instance-Aware Hybrid Mamba-Transformer
Shuaifeng Jiao, Xieyuanli Chen
RetrievalAutonomous DrivingTransformerImagePoint Cloud
🎯 What it does: Proposed InsCMPR, an instance-aware cross-modal pose recognition method, which generates descriptors through pixel-level and instance-level modality alignment as well as a dual-branch hybrid Mamba-Transformer network;
Instance Segmentation-Based Hazard Detection with Lunar South Pole Lighting
Joseph M. Cloud, Jason M. Schuler
SegmentationData SynthesisConvolutional Neural NetworkTransformerImagePhysics Related
🎯 What it does: Study on using instance segmentation models to detect rock hazards in the visual environment of the Moon's south pole
Integrated Motion State Prediction for Sit-to-Stand and Stand-to-Sit Motions Toward Effective Power Assist Control
Kai Ren, Qi An
Robotic IntelligenceRecurrent Neural NetworkBiomedical Data
🎯 What it does: Proposed a sensing method utilizing electrophysiological measurements and deep neural networks to predict the timing of sit-to-stand and stand-to-sit movement initiation, for controlling robotic assistive devices.
Integrating Active Sensing and Rearrangement Planning for Efficient Object Retrieval from Unknown, Confined, Cluttered Environments
Junyong Kim, A. H. Qureshi
Robotic IntelligenceImage
🎯 What it does: Proposed a retrieval planning framework that combines heuristic active perception with Monte-Carlo tree search (MCTS), utilizing a robotic arm with a handheld camera to retrieve target objects in unknown constrained, cluttered environments.
Integrating Field of View in Human-Aware Collaborative Planning
Ya-Chuan Hsu, S. Nikolaidis
Autonomous DrivingReinforcement Learning from Human FeedbackWorld Model
🎯 What it does: A hierarchical online planner is proposed for subtask intent adaptation in human-robot collaboration, considering the limited human field of view (FOV), and its effectiveness is validated through user studies and VR kitchen environments.
Integrating Human-Robot Teaming Dynamics Into Mission Planning Tools for Transparent Tactics in Multi-Robot Human Integrated Teams
Audrey L. Aldridge, M. Novitzky
🎯 What it does: In small-scale experiments, the integration of human-robot collaboration dynamics with the task planning tool (PETAAR) was studied to evaluate the coordination ability of robot operators in multi-robot tasks, comparing two collaboration modes: manually inserting waypoints for each robot and using PETAAR to plan all waypoints at once.
Integrating Learning-Based Manipulation and Physics-Based Locomotion for Whole-Body Badminton Robot Control
Haochen Wang, Dong Xuan
Robotic IntelligenceReinforcement LearningPhysics Related
🎯 What it does: Proposed a hybrid control system named Hamlet for controlling full-body doubles badminton robots;
Integrating Model-Based Control and RL for Sim2Real Transfer of Tight Insertion Policies
Isidoros Marougkas, Kostas E. Bekris
Robotic IntelligenceReinforcement Learning
🎯 What it does: Proposed and verified a strategy integrating model-based control with reinforcement learning for tight plug-and-play tasks, trained in simulation and achieving zero-shot transfer to real systems.
Integrating Multi-Robot Adaptive Sampling and Informative Path Planning for Spatiotemporal Natural Environment Prediction
Siva Kailas, Katia P. Sycara
OptimizationRobotic IntelligenceTime Series
🎯 What it does: Study the application of multi-robot information path planning in adaptive sampling, using spatiotemporal mixed Gaussian processes (STMGP) to determine the most informative sampling locations, and submodular function optimization to plan sampling paths; implement a decentralized two-stage sampling process.
Intelligence Evaluation Methods for Autonomous Vehicles
Jun Zhou, Xiaofan Wang
Autonomous DrivingOptimizationAdversarial Attack
🎯 What it does: Propose a Robust Training-based Comprehensive Evaluation System (RTCE) for quantitatively assessing the temporal intelligence level of autonomous vehicles.
Intelligent Self-Healing Artificial Muscle: Mechanisms for Damage Detection and Autonomous Repair of Puncture Damage in Soft Robotics
Ethan J. Krings, Eric J. Markvicka
Robotic Intelligence
🎯 What it does: Proposed a soft structure embedding liquid metal microdroplets into a silicone rubber elastomer, enabling soft robots to automatically detect damage under high pressure or puncture and achieve self-healing and functional recovery through the formation of a conductive network.
IntelliRMS: A Robotic Manipulation System for Domain-Specific Tasks Using Vision and Language Foundational Models
Chandan Kumar Singh, Rajesh Sinha
Robotic IntelligenceVision Language ModelVision-Language-Action ModelMultimodality
🎯 What it does: Designed and implemented the IntelliRMS system, utilizing vision and language foundation models to achieve instruction-following robot control for domain-specific tasks such as industrial pick-and-place.
Interaction-Driven Updates: 3D Scene Graph Maintenance During Robot Task Execution
Qingfeng Li, Jianwei Niu
Robotic IntelligenceLarge Language ModelWorld ModelGraph
🎯 What it does: Proposes an interaction-driven 3D scene graph maintenance method that integrates an observation point selection module and a dynamic scene maintenance module to continuously update scene information as the robot performs tasks.
Interactive Motion Planning for a 7-DOF Robot
Nicholas Greene, P. Kazanzides
Robotic IntelligenceReinforcement Learning from Human FeedbackImage
🎯 What it does: Proposes an extended Interactive Planning and Supervised Execution (IPSE) system to achieve full remote control of a 7-degree-of-freedom (7-DOF) robot, encoding redundant degrees of freedom using a shoulder-elbow-wrist (SEW) angle map, and embedding robot state information as a 2D image within this angle map to enable interactive motion planning.
Interactive OT Gym: A Reinforcement Learning-Based Interactive Optical Tweezer (OT)-Driven Microrobotics Simulation Platform
Zongcai Tan, Dandan Zhang
Robotic IntelligenceReinforcement LearningPhysics Related
🎯 What it does: Designed an Interactive OT Gym simulation platform based on reinforcement learning, supporting collaborative manipulation by optical tweezers-driven micro-robots, and integrated physical field simulation, tactile feedback, and context-aware shared control strategies
Interactive4D: Interactive 4D LiDAR Segmentation
Ilya Fradlin, Bastian Leibe
SegmentationPoint Cloud
🎯 What it does: Proposes a novel paradigm for interactive 4D LiDAR segmentation, and realizes the first model named Interactive4D that can simultaneously segment multiple objects in overlapping consecutive LiDAR scans in a single iteration
Interface Matters: Comparing First and Third-Person Perspective Interfaces for Bi-Manual Robot Behavioural Cloning
Haining Luo, Y. Demiris
Robotic IntelligenceVideo
🎯 What it does: Investigated the effectiveness of first-person and third-person perspective interfaces in robot shoelace behavior cloning, and explored the impact of interface design on the quality of expert demonstrations.
Internal-Stably Energy-Saving Cooperative Control of Articulated Wheeled Robot with Distributed Drive Units
Yi Yang, Shanshan Xie
OptimizationRobotic Intelligence
🎯 What it does: Proposed a coordinated control algorithm based on equivalent and distribution of driving force for multi-driving units to improve the foldable tracked robot with distributed drive.
Interpretable Active Inference Gait Control Learning
Rudolf J. Szadkowski, J. Faigl
Explainability and InterpretabilityRobotic Intelligence
🎯 What it does: Implement adaptive gait control for hexapod robots in adversarial environments using a self-learning gait dynamics model and the Free Energy Principle (FEP) framework;
Intraoperative 3D Shape Estimation of Magnetic Soft Guidewire
Yiting Zhao, Nan Xiao
Image
🎯 What it does: Introduces a technique utilizing a flexible magnetic tip guidewire to achieve intraoperative three-dimensional shape reconstruction during vascular interventional surgery.
Intraoperative Trocar-Based Eyeball Rotation Estimation Using Only 2D Microscope Images
Junjie Yang, I. M. A. N. Fellow
Pose EstimationImageBiomedical Data
🎯 What it does: By using only 2D microscope images, leveraging the eye's geometric model and the position information of the microscope puncture holes, the rotation of the eye along the x and y axes is calculated to achieve intraoperative eye pose estimation.
Introducing Collaborative Robots as a First Step Towards Autonomous Reprocessing of Medical Equipment
Florian Voigt, Sami Haddadin
Robotic Intelligence
🎯 What it does: This study proposes a framework based on compliant collaborative robots for automated handling and storage of endoscopes after disinfection, addressing the challenge of high-dexterity operations in endoscope reprocessing.
Introducing KUGE: A Simultaneous Control Co-Design Architecture and its Application to Aerial Robotics Development
J. Wauters, G. Crevecoeur
OptimizationComputational EfficiencyRobotic Intelligence
🎯 What it does: Proposed and validated a synchronous control co-design (CCD) architecture KUGE for the dynamic design of tail-sitters
Introspective Loop Closure for SLAM with 4D Imaging Radar
Maximilian Hilger, Achim J. Lilienthal
Autonomous DrivingSimultaneous Localization and MappingPoint Cloud
🎯 What it does: Studied how to utilize 4D imaging radar for loop closure detection in SLAM, focusing on similar and opposite viewpoints.
Inverse Kinematics on Guiding Vector Fields for Robot Path Following
Yu Zhou, Héctor García de Marina
Robotic Intelligence
🎯 What it does: This paper applies inverse kinematics to a guidance vector field to achieve path tracking for autonomous mobile robots. It constructs an error signal using zero-level sets, enabling the robot to converge and move along the path under the guidance of the vector field. Furthermore, inverse kinematics is utilized to ensure the error signal forms a linear system, thereby enabling control of the robot's short-term motion, with a feedforward signal injected to precisely adjust motion behavior along the path. A theoretical and practical solution is proposed for achieving precise short-term control of a constant-speed unicycle on a 2D path. Finally, the theoretical results are validated through flight experiments with a fixed-wing unmanned aerial vehicle.
Inverse Mixed Strategy Games with Generative Trajectory Models
Muchen Sun, Todd Murphey
OptimizationReinforcement LearningAuto EncoderBenchmark
🎯 What it does: Propose an inverse game method that integrates a trajectory generation model with a differentiable hybrid strategy game framework, using CVAE to represent hybrid strategies, inferring high-dimensional multimodal behavior distributions from noisy measurements and adapting in real-time to new observations.
Investigating Security Threats in Multi-Tenant ROS 2 Systems
Lichen Xia, Weisong Shi
Safty and PrivacyRobotic Intelligence
🎯 What it does: In-depth study of security threats in a multi-tenant ROS 2 system, focusing on analyzing vulnerabilities in ROS nodes and topics, designing attack strategies to bypass isolation and security mechanisms, validating attack effectiveness through simulation, and proposing defensive practices.
IRef-VLA: A Benchmark for Interactive Referential Grounding with Imperfect Language in 3D Scenes
Haochen Zhang, Wenshan Wang
Graph Neural NetworkVision Language ModelPoint CloudGraphBenchmark
🎯 What it does: Constructed and released the IRef-VLA dataset for interactive referential localization tasks, containing natural language instructions and navigation goals in 3D scenes.
IROAM: Improving Roadside Monocular 3D Object Detection Learning from Autonomous Vehicle Data Domain
Zhe Wang, Yan Wang
Domain AdaptationAutonomous DrivingTransformerContrastive Learning
🎯 What it does: Propose the IROAM framework, leveraging data from vehicle-mounted and roadside units to enhance monocular 3D object detection at the roadside.
Is Discretization Fusion All You Need for Collaborative Perception?
Kang Yang, Deying Li
Object DetectionAutonomous DrivingVideoPoint Cloud
🎯 What it does: Proposed a anchor-based collaborative perception framework named ACCO for target detection in multi-agent systems; the framework includes anchor feature block (AFB), anchor confidence generator (ACG), and local-global fusion modules (LAAF and SACA)
Is Iteration Worth It? Revisit its Impact in Sliding-Window VIO
Chuchu Chen, Guoquan Huang
Pose EstimationOptimizationSimultaneous Localization and Mapping
🎯 What it does: The first comprehensive study on iterative algorithms in sliding window visual inertial odometry (VIO) was conducted, separately analyzing the impact of relinearization of IMU and camera measurements on system performance.
Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans?
Yuki Shirai, Tao Pang
Robotic IntelligenceOrdinary Differential Equation
🎯 What it does: Analyzes the effectiveness of linear controller synthesis based on contact smoothing in contact-rich operations, and verifies its performance in whole-body experiments with robotic hands.
Iterative Volume Fusion for Asymmetric Stereo Matching
Yuanting Gao, Linghao Shen
Depth EstimationImageBenchmark
🎯 What it does: Propose a two-stage iterative volume fusion network, IVF-AStereo, for addressing visual asymmetry in stereo matching.
Jailbreaking LLM-Controlled Robots
Alexander Robey, George Pappas
Adversarial AttackRobotic IntelligenceTransformerLarge Language ModelText
🎯 What it does: Proposed and implemented the ROBOPAIR algorithm to attack and disable robots controlled by large language models (LLMs), enabling them to perform harmful physical actions.
Joint 3D Point Cloud Segmentation Using Real-Sim Loop: From Panels to Trees and Branches
Tian Qiu, Yu Jiang
SegmentationData SynthesisPoint Cloud
🎯 What it does: Proposes a training data generation method based on Real2Sim L-TreeGen and designs a joint model J-P2TB for joint point cloud segmentation from panels to trees and branches.
Joint Localization and Planning Using Diffusion
L. L. Beyer, S. Karaman
Autonomous DrivingDiffusion modelPoint Cloud
🎯 What it does: Propose an end-to-end localization and path planning framework based on diffusion models, which can generate globally collision-free paths in any known 2D environment by combining self-observed LiDAR scans and target positions.
Jointly Assigning Processes to Machines and Generating Plans for Autonomous Mobile Robots in a Smart Factory
Christopher Leet, Sven Koenig
OptimizationRobotic Intelligence
🎯 What it does: Proposed ACES, which first jointly optimizes the allocation of processes and machines as well as mobile robot path planning.
JORD: A Benchmark Dataset for Off-Road LiDAR Place Recognition and SLAM
Wei Zhou, Gang Wang
Simultaneous Localization and MappingPoint CloudBenchmark
🎯 What it does: Proposed and released the first benchmark dataset JORD specifically designed for airborne LiDAR SLAM, and conducted benchmark testing with multiple advanced methods.
JPG-SLAM: Joint Point-Gaussian Splatting Representation for Dense Dynamic SLAM
Kunrui Huang, Jian Yao
Gaussian SplattingSimultaneous Localization and MappingPoint Cloud
🎯 What it does: Proposed a dense dynamic SLAM system based on Joint Point-Gaussian Splatting, enabling precise pose estimation and dynamic scene reconstruction.
JRN-Geo: A Joint Perception Network Based on RGB and Normal Images for Cross-View Geo-Localization
Hongyu Zhou, Yizhong Zhang
Data SynthesisRetrievalConvolutional Neural NetworkMultimodality
🎯 What it does: This paper proposes a joint perception network called JRN-Geo based on RGB and normal maps, which achieves deep fusion through a dual-branch feature extraction framework combined with a Difference-Aware Fusion Module (DAFM) and a Joint-Constrained Interaction Aggregation (JCIA). It enhances the robustness of cross-perspective geolocation by generating perspective variation samples using 3D geographic augmentation technology.
Juzu Type Gripper That Can Change Both Shape and Firmness
Shunya Hara, Mitsuru Higashimori
Robotic Intelligence
🎯 What it does: Designed and developed a Juzu-type gripper capable of actively altering the shape and stiffness of its fingers, and experimentally validated its effectiveness in pre-shaping and grasping objects of different shapes and sizes.
KALIE: Fine-Tuning Vision-Language Models for Open-World Manipulation Without Robot Data
G. Tang, Kuan Fang
Data SynthesisRobotic IntelligenceSupervised Fine-TuningVision Language ModelImageText
🎯 What it does: KALIE fine-tunes pre-trained vision-language models to predict point-basis affordance representations using natural language instructions and visual observations, enabling robots to perform open-world manipulation tasks.
KALM: Keypoint Abstraction Using Large Models for Object-Relative Imitation Learning
Xiaolin Fang, L. Kaelbling
Large Language ModelVision Language ModelMultimodality
🎯 What it does: Leverage large-scale pre-trained vision-language models to automatically generate task-related and cross-instance consistent keypoints, and train keypoint-conditioned policy models based on these keypoints, enabling robots to generalize across different object poses, camera viewpoints, and object instances with similar functional shapes;
Kalman-Filter-Based Pose Estimation of Cable-Driven Parallel Robots Using Cable-Length Measurements with Colored Noise
Vinh Nguyen, Ryan J. Caverly
Pose Estimation
🎯 What it does: Proposes an Extended Kalman Filter (EKF) framework based on cable length for estimating the end-effector pose of cable-driven parallel robots.
KARMA: Augmenting Embodied AI Agents with Long-and-Short Term Memory Systems
Zixuan Wang, Yiming Gan
Robotic IntelligenceLarge Language ModelPrompt EngineeringRetrieval-Augmented Generation
🎯 What it does: Proposed the KARMA memory system, integrating long-term and short-term memory modules to enhance the planning capabilities of embodied AI agents when performing complex household tasks.
Key-Scan-Based Mobile Robot Navigation: Integrated Mapping, Planning, and Control Using Graphs of Scan Regions
Dharshan Bashkaran Latha, Ömür Arslan
Robotic IntelligenceSimultaneous Localization and MappingPoint CloudGraph
🎯 What it does: Propose a mobile robot navigation framework based on key scans, integrating mapping, planning, and control into a scan area map; verify its effectiveness in 2D cluttered environments through experiments.
Keypoint Detection and Description for Raw Bayer Images
Jiakai Lin, Guoyu Lu
Pose EstimationConvolutional Neural NetworkImage
🎯 What it does: Proposed a keypoint detection and description network that directly processes raw Bayer images, avoiding traditional ISP processing;
KFCalibNet: A KansFormer-Based Self-Calibration Network for Camera and LiDAR
Zejing Xu, Zhe Fu
Pose EstimationAutonomous DrivingOptimizationTransformerImageMultimodalityPoint Cloud
🎯 What it does: Proposed a self-calibration network called KFCalibNet based on KansFormer for extrinsic calibration between camera and LiDAR;
Kinematic-ICP: Enhancing LiDAR Odometry with Kinematic Constraints for Wheeled Mobile Robots Moving on Planar Surfaces
Tiziano Guadagnino, C. Stachniss
Autonomous DrivingOptimizationSimultaneous Localization and MappingPoint Cloud
🎯 What it does: Proposes a LiDAR odometry system that integrates vehicle kinematic constraints into traditional ICP optimization to improve localization accuracy for mobile robots on planar surfaces.
Kineto-Dynamical Planning and Accurate Execution of Minimum-Time Maneuvers on Three-Dimensional Circuits
Mattia Piccinini, F. Biral
Autonomous DrivingOptimization
🎯 What it does: Proposed an artificial racing driver (ARD) capable of online learning of vehicle dynamics, planning, and executing shortest-time actions on 3D tracks;
Kinodynamic Model Predictive Control for Energy Efficient Locomotion of Legged Robots with Parallel Elasticity
Yulun Zhuang, Yanran Ding
OptimizationRobotic Intelligence
🎯 What it does: Proposes a kinodynamic MPC framework that utilizes unidirectional parallel springs (UPS) to enhance the energy efficiency of dynamic legged robots, and adopts a hierarchical control structure: first using a simplified dynamic model MPC for warm-up, then employing full nonlinear centroid dynamics and kinematic constraints for kinodynamic MPC; reduces peak motor torque and energy consumption during the stance phase via UPS, achieving energy-efficient dynamic jumps.
KISS-Matcher: Fast and Robust Point Cloud Registration Revisited
Hyungtae Lim, L. Carlone
Pose EstimationComputational EfficiencyPoint Cloud
🎯 What it does: Developed an open-source C++ library called KISS-Matcher for point cloud registration, integrating a new feature detector Faster-PFH and a graph theory pruning algorithm based on k-core, forming a complete and user-friendly registration pipeline.
Knowledge-Driven Visual Target Navigation: Dual Graph Navigation
Shiyao Li, Feilong Wang
Autonomous DrivingComputational EfficiencyRepresentation LearningGraph Neural NetworkImageGraph
🎯 What it does: Propose a knowledge-driven, lightweight dual graph navigation framework (Dual Graph Navigation), achieving image instance navigation by constructing external and internal knowledge graphs;
Koopman Operator Based Linear Model Predictive Control for Quadruped Trotting
Chun-Ming Yang, Pranav A. Bhounsule
OptimizationRobotic Intelligence
🎯 What it does: Built a high-dimensional linear model based on the Koopman operator for quadruped robot gait control, and achieved high-fidelity tracking and disturbance suppression through linear model predictive control (LMPC).
KUDA: Keypoints to Unify Dynamics Learning and Visual Prompting for Open-Vocabulary Robotic Manipulation
Zixi Liu, Yunzhu Li
Robotic IntelligenceVision Language ModelImageText
🎯 What it does: Built an open-source lexical robot operating system named KUDA, integrating keypoint visual cues with dynamics learning to achieve automatic planning for diverse tasks;
LaB-CL: Localized and Balanced Contrastive Learning for Improving Parking Slot Detection
J. Jeong, I. Yong
Object DetectionData-Centric LearningContrastive Learning
🎯 What it does: Proposes a supervised contrastive learning framework named LaB-CL specifically designed for parking slot detection, aiming to address classification bias caused by data imbalance.
Lab2Car: A Versatile Wrapper for Deploying Experimental Planners in Complex Real-World Environments
Marc Heim, Momchil S. Tomov
Autonomous DrivingOptimization
🎯 What it does: Developed Lab2Car, an expandable wrapper capable of converting trajectory sketches generated by any motion planner into safe, comfortable, and dynamically feasible driving trajectories, enabling originally unsafe planners to be safely tested and optimized in real-world environments.
Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator
Wei-Bin Kou, Yik-Chung Wu
SegmentationAutonomous DrivingOptimizationExplainability and InterpretabilityTransformerImage
🎯 What it does: Proposed a Label Anything Model (LAM) that leverages pre-trained Vision Transformer for feature extraction, and adds Semantic Class Adapter (SCA) and Optimization-oriented Unrolling Algorithm (OptOU) to achieve interpretable, high-fidelity, prompt-free data annotator.
LACNS: Language-Assisted Continuous Navigation in Structured Spaces
Rutong Peng, Mengyin Fu
Autonomous DrivingLarge Language ModelVision Language ModelImageTextMultimodality
🎯 What it does: Proposed the LACNS system, which generates BEV maps using vehicle front-facing cameras, detects intersections with visual language models (VLM), selects navigation frontiers with language models (LLM), achieving continuous space autonomous driving navigation without relying on HD maps
LAFNET: Lightweight Aerial Fire Detection Model for Onboard Edge Computing
Haozhou Zhai, Tianjiang Hu
Object DetectionComputational EfficiencyConvolutional Neural NetworkImage
🎯 What it does: Proposed and implemented a lightweight aerial fire detection model called LAFNET
LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner
Xiaopan Zhang, Jiachen Li
OptimizationLarge Language ModelAgentic AIMultimodalityBenchmark
🎯 What it does: Proposed LaMMA-P, a language model-driven multi-agent PDDL planning framework that combines LM reasoning with traditional heuristic search planning to address long-term tasks, and constructed the MAT-THOR benchmark for multi-level complexity home tasks based on AI2-THOR.
LaMOT: Language-Guided Multi-Object Tracking
Yunhao Li, Libo Zhang
Object TrackingVision Language ModelVideoBenchmark
🎯 What it does: Proposed a language-oriented unified task framework for multi-object tracking, created the LaMOT benchmark dataset, and introduced a concise and effective tracker called LaMOTer.
LamPro: Multi-Prototype Representation Learning for Enhanced Visual Pattern Recognition
Ji-rong Qi, Yang Wang
RecognitionRepresentation LearningContrastive LearningImage
🎯 What it does: Proposed the Label-aware multi-prototype learning method LamPro, which utilizes label information during prototype formation and updating to enhance the quality of visual pattern recognition representations.