IROS 2023 Papers — Page 5
IEEE/RSJ International Conference on Intelligent Robots and Systems · 1195 papers
ETAUS: An Edge and Trustworthy AI UAV System with Self-Adaptivity for Air Quality Monitoring
Chun-Hsian Huang, Ren Wang
ClassificationSafty and PrivacyComputational Efficiency
🎯 What it does: Developed an FPGA-based drone edge AI system called ETAUS for real-time air quality monitoring, which includes a custom neural engine for AQI level classification, a pre-trained model for detecting objects containing private information, and integrated de-identification, encryption functions, and a protection matrix.
Evaluation Metrics of Object Detection for Quantitative System-Level Analysis of Safety-Critical Autonomous Systems
Apurva Badithela, Richard M. Murray
Object DetectionAutonomous Driving
🎯 What it does: Proposes two evaluation metrics based on proposition marking and distance parameterized confusion matrices, and maps detection performance to formal specifications of a closed-loop system through a probabilistic model checking approach, calculating the probability that the system satisfies safety requirements; demonstrates the application of this method in a vehicle-pedestrian example.
Evaluation of a 7-DoFs Robotic Manipulator as Haptic Interface During Planar Reaching Tasks
A. Noccaro, Domenico Formica
Robotic Intelligence
🎯 What it does: Evaluate the applicability of a 7-degree-of-freedom robotic arm as a planar tactile interface for planar task accomplishment, measure human motion and force, and assess disturbances generated by the robot.
Evaluation of Underwater AprilTag Localization for Highly Agile Micro Underwater Robots
Nathalie Bauschmann, R. Seifried
Pose EstimationRobotic Intelligence
🎯 What it does: A method to evaluate the accuracy of underwater AprilTag localization is proposed, focusing on the performance of highly agile micro underwater robots under dynamic motion and calibration media. The experiments verify the importance of calibration and the linear decline of detection accuracy with increasing camera distance, while also identifying the camera that maximizes detection rate during high-speed motion.
EvCenterNet: Uncertainty Estimation for Object Detection Using Evidential Learning
Monish R. Nallapareddy, Abhinav Valada
Object DetectionAutonomous DrivingImagePoint Cloud
🎯 What it does: Propose the EvCenterNet framework, which directly estimates uncertainty in classification and regression using evidential learning, and enhances detection performance through active learning based on heatmap uncertainty.
Event Camera-Based Visual Odometry for Dynamic Motion Tracking of a Legged Robot Using Adaptive Time Surface
Shifan Zhu, Donghyun Kim
Pose EstimationRobotic IntelligenceSimultaneous Localization and MappingImageMultimodalityPoint Cloud
🎯 What it does: Propose a direct sparse visual odometry method combining event cameras and RGB-D for dynamic motion tracking of quadruped robot pose estimation.
EventTransAct: A Video Transformer-Based Framework for Event-Camera Based Action Recognition
Tristan de Blegiers, M. Shah
RecognitionTransformerContrastive LearningVideo
🎯 What it does: Proposed EventTransAct, an event camera action recognition framework based on video Transformer, utilizing spatial embeddings of event frames combined with temporal self-attention, and designing Event-Contrastive Loss along with event-specific data augmentation;
Eversion-Capable Fabric Robot Gripper with Novel Retraction Mechanism
Ahmed Hassan, K. Althoefer
Robotic Intelligence
🎯 What it does: Designed and tested a soft robotic gripper with fabric fingertips capable of expanding, bending, and retracting.
eViper: A Scalable Platform for Untethered Modular Soft Robots
Hsin Cheng, Minjie Chen
Robotic Intelligence
🎯 What it does: Developed an expandable electromagnetic oscillation intelligent piezoelectric soft robot, eViper, and its corresponding open-source simulation framework, SFERS, to study the impact of weight distribution and driving modes on motion in untethered modular soft robots.
evoBOT – Design and Learning-Based Control of a Two-Wheeled Compound Inverted Pendulum Robot
Patrick Klokowski, Sören Kerner
Domain AdaptationRobotic IntelligenceReinforcement LearningBenchmark
🎯 What it does: This paper introduces the evoBOT robot platform, elaborates on its mechanical and electronic design, and proposes a control method based on reinforcement learning for training high-dynamic actions, including steady-state balance and dynamic walking; extensive sim-to-real benchmark testing was conducted between simulation and real environments, along with an initial sim-to-real transfer process; the full robot simulation model is also made publicly available throughout the paper.
EVOLIN Benchmark: Evaluation of Line Detection and Association
K. Ivanov, A. Kornilova
Pose EstimationAutonomous DrivingSimultaneous Localization and MappingImageBenchmark
🎯 What it does: Provides a complete visual line SLAM frontend benchmark, including line detection, line association, and pose error evaluation for RGB and RGB-D images.
Evolutionary-Based Online Motion Planning Framework for Quadruped Robot Jumping
Linzhu Yue, Yunhui Liu
OptimizationRobotic Intelligence
🎯 What it does: Proposed an online jumping motion planning framework based on differential evolution (DE), Latin hypercube sampling, and configuration space (DLC).
Evolving Physical Instinct for Morphology and Control Co-Adaption
Xinglin Chen, Wenjing Yang
Computational EfficiencyRobotic Intelligence
🎯 What it does: Proposed an evolvable instinctive controller to enhance the co-evolution of robot morphology and control, implementing a phase-based finite state machine controller for multi-legged gait control, and conducting experiments on different morphological prototypes using GPU parallel simulation.
Exact Point Cloud Downsampling for Fast and Accurate Global Trajectory Optimization
Kenji Koide, A. Banno
Autonomous DrivingOptimizationPoint Cloud
🎯 What it does: Proposed a point cloud downsampling algorithm that selects a weighted residual subset, making the quadratic registration error function at evaluation points identical to the original point cloud, thereby achieving fast and accurate trajectory optimization.
Experimental Evaluation of a Transparent Operation Mode for a Lower-Limb Exoskeleton Designed for Children with Cerebral Palsy
R. Andrade, Paolo Bonato
Robotic Intelligence
🎯 What it does: A control strategy with a transparent operation mode was implemented and tested on the lower-limb exoskeleton ExoRoboWalker for children with cerebral palsy.
Exploiting Spatio-Temporal Human-Object Relations Using Graph Neural Networks for Human Action Recognition and 3D Motion Forecasting
Dimitrios Lagamtzis, Steffen Schober
RecognitionGraph Neural NetworkVideo
🎯 What it does: Proposed a graph neural network (GNN)-based architecture that integrates human action recognition and motion prediction in industrial human-robot collaboration (HRC) environments, using human-centered 3D information and object labels to construct graph structures.
Exploiting Task Tolerances in Mimicry-Based Telemanipulation
Yeping Wang, M. Gleicher
Robotic Intelligence
🎯 What it does: Implemented a remote operating system that allows robots to autonomously adjust within task tolerance ranges, and compared two teleoperation paradigms: functional imitation and precise imitation.
Exploiting the Kinematic Redundancy of a Backdrivable Parallel Manipulator for Sensing During Physical Human-Robot Interaction
Arda Yiğit, Clément Gosselin
Robotic Intelligence
🎯 What it does: Leveraging the motion redundancy of parallel robots under back-driving conditions, inferring the operator's intention through the use of only motor encoders and forward kinematics solving, and switching between a position controller and a gravity-compensated free-motion guide controller.
Exploring Kinodynamic Fabrics for Reactive Whole-Body Control of Underactuated Humanoid Robots
Alphonsus Adu-Bredu, J. Grizzle
OptimizationRobotic Intelligence
🎯 What it does: Propose and implement the Kinodynamic Fabrics method for real-time multi-task whole-body control in bipedal humanoid robots, evaluated in both simulation and real-world environments.
Exploring Learning-Based Control Policy for Fish-Like Robots in Altered Background Flows
Xiaozhu Lin, Yang Wang
Robotic IntelligenceReinforcement Learning
🎯 What it does: Propose a learning-based control framework that utilizes the FishGym simulation system to train fish-like robots to perform motion control tasks such as approaching targets and maintaining position in non-stationary, unknown background flows, and applies the learned control policies combined with an online estimator to path tracking tasks.
Exploring Social Motion Latent Space and Human Awareness for Effective Robot Navigation in Crowded Environments
J. Ansari, Brojeshwar Bhowmick
Autonomous DrivingRepresentation LearningRobotic Intelligence
🎯 What it does: Learning to utilize the social motion latent space to generate robot control for achieving more effective social navigation
Exploring Visual Pre-training for Robot Manipulation: Datasets, Models and Methods
Ya Jing, Tao Kong
Representation LearningRobotic IntelligenceContrastive Learning
🎯 What it does: Investigate the effectiveness of visual pre-training in robot manipulation tasks and propose the Vi-PRoM approach
Expressing and Inferring Action Carefulness in Human-to-Robot Handovers
Linda Lastrico, J. Santos-Victor
Robotic IntelligenceTime SeriesSequential
🎯 What it does: Implemented an online classifier to distinguish human carefulness during cup handling, and designed an 'expressive' robot controller enabling the robot's motion to convey information based on whether the cup is full or empty, while comparing with a neutral controller.
Extensions to Dynamically-Consistent Collision Reaction Control for Collaborative Robots
Marie Harder, Alexander Dietrich
Robotic Intelligence
🎯 What it does: Designed and verified a dynamically consistent collision reaction controller, achieving reaction motion in specific directions in Cartesian space, and compared with traditional methods.
External Sensor-Less in-Hand Object Position Manipulation for an Under-Actuated Hand Using Data-Driven-Based Methods to Compensate for the Nonlinearity of Self-Locking Mechanism
Hackley Doan, Kenji Tahara
Robotic Intelligence
🎯 What it does: Apply a hybrid analytical model and data-driven methods to analyze internal sensor data, compensate for nonlinear constraints of the self-locking mechanism, propose an object position manipulation framework within the hand, and conduct experimental validation.
Extracting Dynamic Navigation Goal from Natural Language Dialogue
Lanjun Liang, Huaping Liu
Robotic IntelligenceSimultaneous Localization and MappingText
🎯 What it does: Extract dynamic position change information from group chat dialogues to construct a Dynamic Spatiotemporal Map (DSTM), and use this map to assist mobile robots in performing instruction-based navigation tasks within university buildings to locate target individuals.
Extrinsic Calibration of Camera to LIDAR Using a Differentiable Checkerboard Model
L. Fu, Maurice F. Fallon
Pose EstimationAutonomous DrivingOptimizationImagePoint Cloud
🎯 What it does: Proposes a method to achieve external calibration between a camera and LiDAR using only a standard chessboard.
F2BEV: Bird's Eye View Generation from Surround-View Fisheye Camera Images for Automated Driving
Ekta U. Samani, A. Banerjee
GenerationData SynthesisAutonomous DrivingTransformerImage
🎯 What it does: Proposed a baseline method called F2BEV for generating discretized bird's-eye-view (BEV) height maps and semantic segmentation maps from fisheye camera images.
FABRIKv: A Fast, Iterative Inverse Kinematics Solver for Surgical Continuum Robot with Variable Curvature Model
Fuhao Wang, Xiuhong Tang
OptimizationComputational EfficiencyRobotic Intelligence
🎯 What it does: Proposes a fast inverse kinematics solver FABRIKv for a surgical continuum robot with a variable curvature model, first analyzing the robot's deformation and presenting a representation method for the variable curvature model, then improving the FABRIK algorithm to maintain real-time performance under load and correct deformation.
Falcon: A Wide-and-Deep Onboard Active Vision System
Masahiro Hirano, Yuji Yamakawa
Object TrackingAutonomous DrivingImageVideo
🎯 What it does: Designed and implemented an active vision system called Falcon, integrating an electric zoom lens, high-speed camera, and servo mirrors, achieving high-resolution imaging from close to distant ranges. Proposed a mapping-based external camera calibration method and a lightweight visual feedback algorithm for object tracking. Subsequently validated the system performance in indoor experiments and achieved continuous high-resolution imaging of curved mirrors during vehicle movement.
Fast Asymptotically Optimal Path Planning in Dynamic, Uncertain Environments
Lu Huang, Xingjian Jing
Optimization
🎯 What it does: Propose Fast Adaptive Tree (FAT), a sampling-based path planner that is incrementally optimal in dynamic uncertain environments.
Fast Bi-Monocular Visual Odometry Using Factor Graph Sparsification
César Debeunne, Damien Vivet
Pose EstimationOptimizationComputational Efficiency
🎯 What it does: Proposes an indirect dual monocular visual odometry method based on sliding window optimization, aiming to maintain problem sparsity and reduce computational burden under low-light conditions, applicable to scenarios such as lava tunnel exploration.
Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports Using Graph Learning
Prajit KrisshnaKumar, Souma Chowdhury
OptimizationGraph Neural NetworkReinforcement LearningGraph
🎯 What it does: Proposed a graph reinforcement learning-based urban aerial mobility (UAM) vertical runway scheduling management (UAM-VSM) method, which uses graph convolutional networks (GCN) to extract features of runway positions and aircraft, and employs perceptron layers to determine actions such as staying, cruising, taking off, or landing.
Fast Point to Mesh Distance by Domain Voxelization
Geordan Gutow, H. Choset
Computational EfficiencyMesh
🎯 What it does: Proposed a voxel-based point-to-triangle mesh distance calculation method
FATROP: A Fast Constrained Optimal Control Problem Solver for Robot Trajectory Optimization and Control
Lander Vanroye, W. Decré
OptimizationRobotic Intelligence
🎯 What it does: Proposed a fast constrained optimal control problem solver named FATROP for robot trajectory optimization and control.
FeatDANet: Feature-level Domain Adaptation Network for Semantic Segmentation
Jiao Li, Jiamao Li
SegmentationDomain AdaptationConvolutional Neural NetworkImage
🎯 What it does: Proposes an unsupervised domain adaptation network called FeatDANet, which focuses on aligning feature-level domain distributions at each encoder layer to achieve semantic segmentation transfer from synthetic data to real data.
Feature Explanation for Robust Trajectory Prediction
Xukai Zhai, Zhishuai Yin
Autonomous DrivingExplainability and InterpretabilityTransformerTime SeriesBenchmark
🎯 What it does: Proposed and implemented a Parallel Explainable Transformer (PXT) framework that uses a dual-branch encoder to separate road information from historical trajectories, and employs explainability methods to select the most contributing features for trajectory prediction.
Feature-based Visual Odometry for Bronchoscopy: A Dataset and Benchmark
Jianning Deng, Mohsen Khadem
Pose EstimationConvolutional Neural NetworkSimultaneous Localization and MappingVideoBiomedical DataBenchmark
🎯 What it does: Developed a bronchoscopy visual odometry dataset containing 34 video clips and over 23,000 frames, along with benchmark experiments.
Feedback Motion Prediction for Safe Unicycle Robot Navigation
Aykut Isleyen, Ömür Arslan
Robotic Intelligence
🎯 What it does: Proposes a novel conical feedback motion prediction method for two-wheel differential robots (modeled as a kinematic unicycle model), and applies it to reference governors to achieve safe obstacle navigation;
Few-Shot Segmentation and Semantic Segmentation for Underwater Imagery
Imran Kabir, Md. Alimoor Reza
SegmentationMeta LearningConvolutional Neural NetworkImageBenchmark
🎯 What it does: Proposes a new dense pixel-level annotated dataset based on underwater animals and addresses the few-shot segmentation and semantic segmentation tasks on this dataset
Finding Biomechanically Safe Trajectories for Robot Manipulation of the Human Body in a Search and Rescue Scenario
Elizabeth Peiros, Michael C. Yip
OptimizationSafty and PrivacyRobotic IntelligenceBiomedical Data
🎯 What it does: Designed and verified a robot trajectory planning method considering biomechanical safety constraints in search and rescue scenarios, for repositioning limbs of unconscious human victims to achieve safe extraction.
Finding the Goal: Insect-Inspired Spiking Neural Network for Heading Error Estimation
Thorben Schoepe, Elisabetta Chicca
Robotic IntelligenceSpiking Neural Network
🎯 What it does: Proposed and implemented a spiking neural network model based on insect navigation for estimating heading error, and validated its functionality on a simulated robot platform.
FingerTac - An Interchangeable and Wearable Tactile Sensor for the Fingertips of Human and Robot Hands
Prathamesh Sathe, Shigeki Sugano
Robotic Intelligence
🎯 What it does: Developed a wearable fingertip tactile sensor that is interchangeable between human and robotic fingers and can capture triaxial force vectors on the fingertips
FISS+: Efficient and Focused Trajectory Generation and Refinement Using Fast Iterative Search and Sampling Strategy
Shuo Sun, Marcelo H. Ang
Autonomous DrivingOptimizationComputational EfficiencyBenchmark
🎯 What it does: Proposes a two-stage coarse-to-fine sampling-based trajectory planning framework
Flexible Gear Assembly with Visual Servoing and Force Feedback
J. Ming, M. Caccamo
Object DetectionData SynthesisRobotic IntelligenceConvolutional Neural NetworkReinforcement LearningImage
🎯 What it does: This paper proposes a visual-guided two-stage gear assembly method with force feedback, first using YOLO for rough localization of the workpiece, and then using deep reinforcement learning to complete the insertion.
Flexible Handover with Real-Time Robust Dynamic Grasp Trajectory Generation
Gu Zhang, Cewu Lu
Robotic IntelligenceBenchmark
🎯 What it does: Proposed a flexible hand interaction method with high success rates, enabling robots to grasp objects in complex continuous motion scenarios.
Flexible Multi-DoF Aerial 3D Printing Supported with Automated Optimal Chunking
Marios-Nektarios Stamatopoulos, G. Nikolakopoulos
OptimizationRobotic IntelligencePhysics Related
🎯 What it does: Proposes a block-based distributed 3D printing framework for unmanned aerial vehicles (UAVs), which divides models into manageable blocks through an optimization process, allocates them to UAVs for partial printing, and achieves fully autonomous operation.
FM-Loc: Using Foundation Models for Improved Vision-Based Localization
Reihaneh Mirjalili, Wolfram Burgard
Pose EstimationTransformerLarge Language ModelVision Language ModelImage
🎯 What it does: Proposes an FM-Loc visual localization method based on foundation models (GPT-3 and CLIP), constructing semantic image descriptors to enhance the robustness of indoor visual localization
FogROS2-SGC: A ROS2 Cloud Robotics Platform for Secure Global Connectivity
Kai-Peng Chen, Ken Goldberg
Safty and PrivacyRobotic Intelligence
🎯 What it does: Introduce and implement FogROS2-SGC, a ROS2 cloud robotics platform that enables secure global connectivity across different physical locations, networks, and DDS implementations.
Force Map: Learning to Predict Contact Force Distribution from Vision
Ryo Hanai, T. Ogata
Data SynthesisRobotic IntelligenceConvolutional Neural NetworkImage
🎯 What it does: Utilize visual prediction of contact force distribution (force map) to plan the lifting direction, thereby reducing disturbances in stacked objects.
Force-Based Pose Regulation of a Cable-Suspended Load Using UAVs with Force Bias
C. Gabellieri, Antonio Franchi
OptimizationRobotic Intelligence
🎯 What it does: Studied the impact of force measurement/estimation errors on force-based cooperative control of a beam-shaped load suspended by two drones via cables; first calculated the system's equilibrium configuration; then proved that inducing internal forces within the load can enhance the robustness of the load's attitude error to force deviations; finally proposed a method to achieve zero load position error.
Formal Composition of Robotic Systems as Contract Programs
Mason Nakamura, Stuart Russell
OptimizationRobotic Intelligence
🎯 What it does: Proposes a meta-reasoning framework that formalizes robot systems as contract programs, incorporating programming constructs with functional, conditional, and cyclic semantics.
Forward/Inverse Kinematics Modeling for Tensegrity Manipulator Based on Goal-Conditioned Variational Autoencoder
Yuhei Yoshimitsu, Shuhei Ikemoto
Robotic IntelligenceAuto Encoder
🎯 What it does: A data-driven approach is used to model the forward and inverse kinematics of a high-redundancy tension structure manipulator, training a VAE-based kinematic model and extracting sub-networks for forward kinematics, inverse kinematics, and null space.
FPECMV: Learning-Based Fault-Tolerant Collaborative Localization Under Limited Connectivity
Rong Ou, Tin Lun Lam
Convolutional Neural NetworkSimultaneous Localization and Mapping
🎯 What it does: Propose an FPECMV algorithm for fault-tolerant collaborative localization under limited connectivity;
FPGADDS: An Intra-FPGA Data Distribution Service for ROS 2 Robotics Applications
Christian Lienen, M. Platzner
Autonomous DrivingRobotic Intelligence
🎯 What it does: Designed and implemented fpgaDDS, a lightweight data distribution service for hardware-mapped ROS 2 nodes, and evaluated its performance in examples and autonomous driving case studies.
FRoGGeR: Fast Robust Grasp Generation via the Min-Weight Metric
Albert Li, A. Ames
OptimizationRobotic IntelligenceMesh
🎯 What it does: Fast generation of robust and precise grasping plans
From “Thumbs Up” to “10 out of 10”: Reconsidering Scalar Feedback in Interactive Reinforcement Learning
Hang Yu, E. Short
Reinforcement Learning from Human FeedbackReinforcement Learning
🎯 What it does: Compared the effects of scalar feedback and binary feedback in interactive reinforcement learning, and proposed the STEADY method to enhance the learning performance of scalar feedback.
From Crowd Motion Prediction to Robot Navigation in Crowds
S. Poddar, S. Srinivasa
OptimizationRobotic IntelligenceGenerative Adversarial Network
🎯 What it does: Integrate the S-GAN motion prediction model into a model predictive controller (MPC) in the laboratory, deploy it on a self-balancing robot, and test its navigation performance under different crowd behaviors.
From Temporal-Evolving to Spatial-Fixing: A Keypoints-Based Learning Paradigm for Visual Robotic Manipulation
Kévin Riou, P. Callet
Robotic Intelligence
🎯 What it does: Propose a hierarchical behavior cloning method that decomposes traditional BC into high-level planning to convert initial observations into spatial waypoints, and low-level execution of predefined primitives to reach waypoints.
Fully Proprioceptive Slip-Velocity-Aware State Estimation for Mobile Robots via Invariant Kalman Filtering and Disturbance Observer
Xihang Yu, Maani Ghaffari
Robotic IntelligenceSimultaneous Localization and Mapping
🎯 What it does: Developed a full slip velocity perception state estimator based on complete self-perception, utilizing invariant observer design theory and disturbance observer (DOB) to achieve real-time estimation of slip velocity in mobile robots.
FUSE-D: Framework for UAV System-Parameter Estimation with Disturbance Detection
Christoph Böhm, Stephan Weiss
Anomaly DetectionAutonomous Driving
🎯 What it does: Propose the FUSE-D framework, integrating online system identification, sensor self-calibration, and external disturbance analysis. Based on a rotor speed-level system model and a single global attitude sensor, it estimates rotor geometry, mass, moment of inertia, rotor aerodynamic characteristics, as well as external forces and their points of application.
Fusing Visual Appearance and Geometry for Multi-Modality 6DoF Object Tracking
Manuel Stoiber, Rudolph Triebel
Object TrackingPose EstimationVideoMultimodality
🎯 What it does: Developed a multi-modal 6DoF object tracker that integrates visual appearance and geometric information for real-time target pose estimation.
FVLoc-NeRF : Fast Vision-Only Localization within Neural Radiation Field
Wenzhi Guo, Lijun Chen
Pose EstimationRetrievalRobotic IntelligenceNeural Radiance FieldImage
🎯 What it does: Propose a fast visual localization framework called FVLoc-NeRF that uses only RGB monocular images, leveraging NeRF to encode 3D geometry and environmental appearance for robot localization.
Game-Theoretical Approach to Multi-Robot Task Allocation Using a Bio-Inspired Optimization Strategy
Shengkang Chen, Fumin Zhang
OptimizationRobotic Intelligence
🎯 What it does: Propose a game theory-based multi-robot task allocation method, considering self-interested robots that do not share utility functions, and design a utility function incorporating conflict penalties and path costs. Task allocation is achieved through consensus communication and SUSD search.
GAPSLAM: Blending Gaussian Approximation and Particle Filters for Real-Time Non-Gaussian SLAM
Qiangqiang Huang, John J. Leonard
OptimizationSimultaneous Localization and MappingTabular
🎯 What it does: Combine Gaussian approximation with particle filters to real-time infer the edge posterior distribution in SLAM, enabling the expression and computational scalability of non-Gaussian posteriors.
gatekeeper: Online Safety Verification and Control for Nonlinear Systems in Dynamic Environments
Devansh R. Agrawal, Dimitra Panagou
🎯 What it does: Developed a real-time, lightweight gatekeeper algorithm integrated into path planning and feedback controllers to verify the safety executability of the trajectory
Gaussian Max-Value Entropy Search for Multi-Agent Bayesian Optimization
Haitong Ma, Na Li
Optimization
🎯 What it does: Proposed and implemented Gaussian Max-value Entropy Search for multi-agent Bayesian optimization.
GelSight Svelte: A Human Finger-Shaped Single-Camera Tactile Robot Finger with Large Sensing Coverage and Proprioceptive Sensing
Jialiang Zhao, E. Adelson
Robotic IntelligenceConvolutional Neural NetworkImage
🎯 What it does: Propose GelSight Svelte, a curved-surface, human-like fingertip, single-camera tactile sensor, and estimate fingertip bending and torsion moments using convolutional neural networks, validating its tactile and proprioceptive capabilities through colloidal deformation experiments and grasping tasks.
Generalized Few-shot Semantic Segmentation for LiDAR Point Clouds
Pengze Wu, Yu Hu
SegmentationRepresentation LearningMeta LearningPoint Cloud
🎯 What it does: Proposes a generic few-shot semantic segmentation method based on LiDAR point cloud data that can simultaneously predict base classes and novel classes.
Generalized Robot Dynamics Learning and Gen2Real Transfer
Dengpeng Xing, Bo Xu
Knowledge DistillationRobotic IntelligenceTransformerSequential
🎯 What it does: Learned a general model covering a large variety of robot dynamics, and proposed the Gen2Real method to transfer the general model generated in simulation to specific physical robots.
Generalizing Surgical Instruments Segmentation to Unseen Domains with One-to-Many Synthesis
An-Chi Wang, Hongliang Ren
SegmentationData SynthesisDomain AdaptationImageBiomedical Data
🎯 What it does: Synthetic surgical instrument segmentation datasets are synthesized using minimal source images through transformations, pooling, and various hybrid techniques, and models are trained on this dataset to improve generalization performance on unseen domains.
Generating Executable Action Plans with Environmentally-Aware Language Models
Maitrey Gramopadhye, D. Szafir
GenerationRobotic IntelligenceTransformerLarge Language ModelScore-based ModelText
🎯 What it does: Propose a scheme to integrate environmental objects and their relationships as additional inputs into large language models (LLMs), generating executable and environment-matching action plans.
Generating Scenarios from High-Level Specifications for Object Rearrangement Tasks
S. V. Waveren, Danica Kragic
GenerationData SynthesisRobotic Intelligence
🎯 What it does: Generate training scenarios for object rearrangement tasks based on high-level specifications, sort scenarios according to difficulty, and use a generative model based on spatial logic specifications to produce spatially structured scenarios that meet specifications and desired difficulty levels.
Generation of Time-Varying Impedance Attacks Against Haptic Shared Control Steering Systems
Alireza Mohammadi, Hafiz Abid Mahmood Malik
Autonomous DrivingAdversarial Attack
🎯 What it does: Studies the attack methods where attackers exploit time-varying impedance to disrupt human-vehicle interaction dynamics, leading to instability in the vehicle steering system.
Geometric Fault-Tolerant Control of Quadrotors in Case of Rotor Failures: An Attitude Based Comparative Study
Jennifer Yeom, Giuseppe Loianno
Robotic Intelligence
🎯 What it does: Propose a fault-tolerant control strategy for quadrotors applicable to both single and dual full rotor failures, enhanced based on the classical geometric tracking controller on SO(3)×ℝ^3.
Geometric Gait Optimization for Inertia-Dominated Systems with Nonzero Net Momentum
Yanhao Yang, Ross L. Hatton
OptimizationPhysics Related
🎯 What it does: Proposed a kinematic and momentum dual gait optimization algorithm for inertial dominance systems with non-zero net momentum, demonstrating its effectiveness in forward and turning motions in systems with/without fluid added mass.
Geometrically Consistent Monocular Metric-Semantic 3D Mapping for Indoor Environments with Transparent and Reflecting Objects
M. Mohrat, S. Kolyubin
SegmentationPose EstimationDepth EstimationConvolutional Neural NetworkSimultaneous Localization and MappingPoint CloudMesh
🎯 What it does: Developed a geometric consistency metric semantic 3D mapping pipeline based on a monocular camera, capable of generating high-quality point clouds in indoor environments containing transparent and reflective objects.
Global Localization in Unstructured Environments Using Semantic Object Maps Built from Various Viewpoints
Jacqueline Ankenbauer, J. How
Autonomous DrivingOptimizationGraph Neural NetworkSimultaneous Localization and MappingImagePoint Cloud
🎯 What it does: Proposes a framework for achieving vehicle global localization and guided relocalization in unstructured environments by associating and registering the vehicle's local semantic object maps with compact semantic reference maps.
Global Localization: Utilizing Relative Spatio-Temporal Geometric Constraints from Adjacent and Distant Cameras
Mohammad Altillawi, Ziyuan Liu
Pose EstimationSimultaneous Localization and MappingImage
🎯 What it does: Leverages relative spatiotemporal geometric constraints from adjacent and distant cameras to estimate 6-DoF camera pose from a single image using a deep network.
Global Map Assisted Multi-Agent Collision Avoidance via Deep Reinforcement Learning around Complex Obstacles
Yuanyuan Du, Shuguang Cui
Autonomous DrivingRobotic IntelligenceReinforcement LearningWorld Model
🎯 What it does: Proposed a global map-assisted multi-agent collision avoidance algorithm that utilizes a distance map to simultaneously consider obstacles and other agents.
GloPro: Globally-Consistent Uncertainty-Aware 3D Human Pose Estimation & Tracking in the Wild
Simon Schaefer, Stefan Leutenegger
Object TrackingPose EstimationMesh
🎯 What it does: Proposes the GloPro framework, which can predict the uncertainty distribution of 3D body meshes that include body shape, pose, and root position, achieving this through an effective fusion of visual cues and learned motion models.
GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning
Yaru Niu, Liangjun Zhang
Robotic IntelligenceReinforcement Learning
🎯 What it does: Proposed and implemented a robot water scooping task based on goal-conditioned reinforcement learning, and designed the GOATS curriculum reinforcement learning method.
GP-Guided MPPI for Efficient Navigation in Complex Unknown Cluttered Environments
Ihab S. Mohamed, Lantao Liu
Autonomous DrivingOptimizationReinforcement Learning
🎯 What it does: Proposed an online learning control strategy GP-MPPI based on sparse Gaussian processes (SGP), combining MPPI with SGP for efficient navigation in unknown, crowded environments.
GraNet: A Multi-Level Graph Network for 6-DoF Grasp Pose Generation in Cluttered Scenes
Haowen Wang, Chungang Zhuang
Pose EstimationRobotic IntelligenceGraph Neural NetworkPoint Cloud
🎯 What it does: Propose GraNet—a 6-DoF object-agnostic grasping pose generation framework based on multi-layer graph networks, which converts point cloud scenes into multi-layer graphs and propagates features through graph neural networks.
Graph Matching Optimization Network for Point Cloud Registration
Qianliang Wu, Jian Yang
Pose EstimationOptimizationPoint Cloud
🎯 What it does: Proposed a graph matching optimization-based network (GMONet), which explicitly imposes isometric constraints in the dense layer via a graph matching optimizer to enhance feature representation for point cloud registration;
Graph-Based Global Robot Localization Informing Situational Graphs with Architectural Graphs
Muhammad Shaheer, Holger Voos
Robotic IntelligenceGraph Neural NetworkSimultaneous Localization and MappingPoint CloudGraph
🎯 What it does: Proposes a scheme for multi-legged robot localization using architectural floor plans. First, the architectural floor plan is converted into an architectural graph (A-Graph). Subsequently, during robot movement, the robot estimates an online situational graph (S-Graph) based on sensor data. By performing graph-to-graph matching, the S-Graph is aligned and merged with the A-Graph to obtain an information-rich situational graph (is-Graph), thereby achieving global localization.
Graph-Based View Motion Planning for Fruit Detection
Tobias Zaenker, Maren Bennewitz
Graph Neural NetworkAgriculture Related
🎯 What it does: Proposes a graph-based perspective motion planner for effectively monitoring and discovering fruits in bell pepper plants.
Graph-Based Visual-Kinematic Fusion and Monte Carlo Initialization for Fast-Deployable Cable-Driven Robots
R. Khorrambakht, Stephan Weiss
Robotic IntelligenceSimultaneous Localization and MappingImageTime Series
🎯 What it does: A unified localization and calibration system based on statistical fusion was constructed using vehicle-mounted cameras and kinematic sensors, employing Monte Carlo initialization and factor graph representation to simultaneously identify kinematic parameters, visual odometry scale, and their uncertainty.
Grasp Region Exploration for 7-DoF Robotic Grasping in Cluttered Scenes
Zibo Chen, Wei-Shi Zheng
Pose EstimationRobotic IntelligencePoint Cloud
🎯 What it does: Proposes a grasp region exploration module and a grasp region attention module to enhance point cloud density around grasp points in cluttered scenes and dynamically aggregate features, thereby improving the grasping performance of 7-DoF robotic hands.
Grasp Stability Assessment Through Attention-Guided Cross-Modality Fusion and Transfer Learning
Zhuangzhuang Zhang, Q. Cao
Domain AdaptationRobotic IntelligenceConvolutional Neural NetworkTransformerMultimodality
🎯 What it does: Proposed an attention-guided cross-modal fusion architecture to comprehensively integrate visual and tactile features for evaluating grasp stability
Grasp State Classification in Agricultural Manipulation
Benjamin Walt, Girish Krishnan
ClassificationTime SeriesAgriculture Related
🎯 What it does: Studied different grasping states (successful grasping, sliding, grasping failure) during fruit harvesting in agricultural environments, and constructed a learning-based classifier using low-cost sensors (IMU and IR reflection).
GVCCI: Lifelong Learning of Visual Grounding for Language-Guided Robotic Manipulation
Junghyun Kim, Byoung-Tak Zhang
Object DetectionRobotic IntelligenceMeta LearningVision-Language-Action ModelImageTextMultimodality
🎯 What it does: Proposed the GVCCI lifelong learning framework, which utilizes object detection to generate synthetic instructions and continuously trains a visual localization model without human supervision, aiming to enhance language-guided robot manipulation performance.
HALO: A Safe, Coaxial, and Dual-Ducted UAV Without Servo
Haotian Li, Fu Zhang
🎯 What it does: Designed and verified a novel drone HALO, utilizing a swashplateless mechanism to achieve pitch and roll control, and employing a coaxial dual-channel ducted fan design to enhance safety and aerodynamic efficiency.
Hand Design Approach for Planar Fully Actuated Manipulators
Keegan Nave, Cindy Grimm
Robotic IntelligenceBenchmark
🎯 What it does: Proposed a fully active planar manipulator design method tailored for specific grasping actions
HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose Annotations, Affordances, and Reconstructions
Andrew Guo, Stan Birchfield
Pose EstimationRobotic IntelligenceImageVideoMeshBenchmark
🎯 What it does: Created the HANDAL dataset for category-level object pose estimation and functional prediction, focusing on tool-like objects graspable by robots. It employs a single camera and semi-automated processing to achieve high-quality 3D annotations, providing 308k frames, 212 objects, 17 categories, and full 3D reconstructed meshes.
Haptic Dataset Augmentation with Subjective QoE Labels using Conditional Generative Adversarial Network
Zican Wang, Eckehard G. Steinbach
Data SynthesisGenerative Adversarial Network
🎯 What it does: Proposed a GAN-based generative method to automatically expand the subjective tactile QoE dataset, avoiding time-consuming experiments.
Hardware-in-the-Loop Simulation of Vehicle-Manipulator Systems for Physical Interaction Tasks
Hemjyoti Das, Christian Ott
Robotic IntelligencePhysics Related
🎯 What it does: A method for impedance matching is proposed to match the dynamics of the end-effector of a fixed-base robotic arm with the dynamics of the target vehicle-robotic arm system (VMS) in hardware-in-the-loop simulation, considering redundant null-space dynamics to ensure that the torque applied to the environment is consistent with the simulation system, and the method is validated using a suspended drone robotic arm.
Harnessing the Power of Human Biomechanics in Force-Position Domain: A 3D Passivity Index Map for Upper Limb Physical Human-(Tele) Robot Interaction
Xingyuan Zhou, S. F. Atashzar
Robotic IntelligenceBiomedical Data
🎯 What it does: This paper constructs a three-dimensional inability index map, quantitatively evaluates the inability margin of the upper limb in the force-position domain, and explores the effects of synergistic contraction levels, interaction frequency, and geometric direction on the inability margin.
Helical Propulsion in Low-Re Numbers with Near-Zero Angle of Attack
Leendert-Jan W. Ligtenberg, Islam S. M. Khalil
Physics Related
🎯 What it does: By establishing a simplified one-dimensional model of UHMD under low Reynolds numbers, the gap with rotating permanent magnets is predicted and adjusted to achieve constrained behavior at near-zero angles of attack, and the method's feasibility for zero-sinking swimming is validated through numerical simulations and experiments.
HELSA: Hierarchical Reinforcement Learning with Spatiotemporal Abstraction for Large-Scale Multi-Agent Path Finding
Zhaoyi Song, Xiang Cheng
Reinforcement Learning
🎯 What it does: A hierarchical reinforcement learning framework is employed to address large-scale multi-agent path planning, leveraging spatial and temporal abstraction to enhance sampling efficiency.