IROS 2024 Papers — Page 7
IEEE/RSJ International Conference on Intelligent Robots and Systems · 1581 papers
FDNet: Feature Decoupling Framework for Trajectory Prediction
Yuhang Li, Guoren Wang
Autonomous DrivingRepresentation LearningSequential
🎯 What it does: Proposed FDNet, a feature disentanglement framework for trajectory prediction
Feasibility-Guided Safety-Aware Model Predictive Control for Jump Markov Linear Systems
Zakariya Laouar, Zachary Sunberg
Optimization
🎯 What it does: A feasibility-guided, safety-oriented model predictive control (MPC) framework is designed for jump Markov linear systems (JMLS), capable of generating control strategies under uncertain mode switching and mode estimation conditions.
Feasible Region Construction by Polygon Merging for Continuous Bipedal Walking*
Chao Li, Zhihong Jiang
OptimizationRobotic IntelligenceMesh
🎯 What it does: Constructing a feasible region for continuous bipedal walking using polygon merging techniques
FEDORA: A Flying Event Dataset fOr Reactive behAvior
Amogh Joshi, Kaushik Roy
Data SynthesisImageMultimodalityTime SeriesBenchmark
🎯 What it does: Constructed a fully synthetic flight event dataset named FEDORA, containing raw data from frame cameras, event cameras, and IMU, and providing high-frequency depth, pose, and optical flow ground truth for multi-perception tasks.
FedRC: A Rapid-Converged Hierarchical Federated Learning Framework in Street Scene Semantic Understanding
Wei-Bin Kou, Yik-Chung Wu
SegmentationAutonomous DrivingFederated LearningImage
🎯 What it does: Propose a fast-converging hierarchical federated learning framework, FedRC, to address the generalization problem caused by data heterogeneity between cities in street scene semantic understanding.
Feeling Optimistic? Ambiguity Attitudes for Online Decision Making
J. Beard, Yu Gu
Optimization
🎯 What it does: Proposed and implemented the Ambiguity Attitude Graph Search (AAGS) algorithm to improve online decision-making by incorporating an adjustable ambiguity attitude when facing multiple possible transition models;
Feelit: Combining Compliant Shape Displays with Vision-Based Tactile Sensors for Real-Time Teletaction
Oscar Yu, Yu She
Robotic IntelligenceImage
🎯 What it does: Designed and implemented a low-cost remote tactile device (Feelit) that utilizes real-time information from visual-based tactile sensors, achieving tactile transmission through physical 3D surface reconstruction and shear displacement, with its performance validated in shape recognition and relative weight identification experiments.
Few-shot Transparent Instance Segmentation for Bin Picking
Anoop Cherian, Tim K. Marks
SegmentationData SynthesisRobotic IntelligenceMeta LearningConvolutional Neural NetworkImage
🎯 What it does: This paper proposes a few-shot transparent instance segmentation method called TrInSeg for robot bin picking scenarios.
FF-SRL: High Performance GPU-Based Surgical Simulation For Robot Learning
D. Dall'Alba, P. Korzeniowski
Robotic IntelligenceReinforcement LearningBiomedical Data
🎯 What it does: Proposed FF-SRL, a high-performance surgical simulation environment with a fully GPU-based implementation.
FI-SLAM: Feature Fusion and Instance Reconstruction for Neural Implicit SLAM
Xingshuo Wang, Xuanhua Chen
Neural Radiance FieldSimultaneous Localization and Mapping
🎯 What it does: Proposed and implemented a dense semantic instance SLAM system FI-SLAM based on neural implicit representations;
Fine Manipulation Using a Tactile Skin: Learning in Simulation and Sim-to-Real Transfer
Ulf Kasolowsky, Berthold Bäuml
Domain AdaptationRobotic IntelligenceReinforcement Learning
🎯 What it does: Achieving fine manipulation using tactile skin with spatial resolution on a multi-fingered robot hand through deep reinforcement learning; proposing a tactile skin model that considers fingertip softness and enables contact diffusion across multiple sensing units, with self-contained parameter calibration without external tools; learning challenging fine manipulation tasks such as rolling marbles and bolts between two fingers in a simulated environment, and demonstrating successful transfer to a real robot hand.
Fine-tuning the Diffusion Model and Distilling Informative Priors for Sparse-view 3D Reconstruction
Jiadong Tang, Mengyin Fu
GenerationKnowledge DistillationPrompt EngineeringDiffusion modelImageText
🎯 What it does: Fine-tune pre-trained diffusion models conditioned on coarse rendering results and text prompts to generate 3D-aware images, and design a semantic switcher to filter mismatched images, distilling valuable priors into 3D reconstruction models.
Finetuning Pre-trained Model with Limited Data for LiDAR-based 3D Object Detection by Bridging Domain Gaps
J. Jang, Jinkyu Kim
Object DetectionDomain AdaptationAutonomous DrivingKnowledge DistillationSupervised Fine-TuningPoint Cloud
🎯 What it does: Under limited target domain data (about 100 LiDAR frames), the Domain Adaptive Distill-Tuning (DADT) method is proposed to fine-tune pre-trained models. By employing a teacher-student architecture, object-level and context-level representations are aligned to maintain expressiveness and prevent overfitting.
Fingertip Tactile Sensor for Detecting Rope Slip
T. Koga, Shunsuke Kudoh
ClassificationRobotic IntelligenceTime Series
🎯 What it does: Developed a 3×3 tactile sensor matrix covering an elastic grip to detect rope slippage on robot fingers and created a corresponding training dataset for learning.
Fixing symbolic plans with reinforcement learning in object-based action spaces
Christopher Thierauf, Matthias Scheutz
Robotic IntelligenceReinforcement Learning
🎯 What it does: Adopted a reinforcement learning method based on an action space defined by object trajectories, replacing the traditional robot joint action space;
Flexible and Topological Consistent Local Replanning for Multirotors
Dong Wang, Fei Gao
OptimizationRobotic Intelligence
🎯 What it does: Propose a flexible and topology-consistent local replanning framework for multirotor UAVs.
Flexible Informed Trees (FIT*): Adaptive Batch-Size Approach in Informed Sampling-Based Path Planning
Liding Zhang, A. Knoll
OptimizationRobotic Intelligence
🎯 What it does: Proposes FIT*, a sampling-based path planning algorithm that flexibly adjusts batch size to enhance initial path convergence speed and later optimization efficiency.
FlexLoc: Conditional Neural Networks for Zero-Shot Sensor Perspective Invariance in Object Localization with Distributed Multimodal Sensors
Jason Wu, Mani Srivastava
Object DetectionMultimodality
🎯 What it does: Propose FlexLoc, which utilizes conditional neural networks to generate subsets of model weights at runtime based on node poses, achieving zero-shot sensor-invariant target localization;
Flight Structure Optimization of Modular Reconfigurable UAVs
Yao Su, Hangxin Liu
Optimization
🎯 What it does: Reconfigure large modular drone swarms using genetic algorithms to generate overactuated flight structures with superior dynamic performance.
FlowTrack: Point-level Flow Network for 3D Single Object Tracking
Shuo Li, Zheng Fang
Object TrackingAutonomous DrivingOptical FlowVideoPoint Cloud
🎯 What it does: Proposed a 3D single-target tracking method called FlowTrack based on point-level flow for multi-frame information
Fly by Book: How to Train a Humanoid Robot to Fly an Airplane using Large Language Models
Hyungjoo Kim, David Hyunchul Shim
Robotic IntelligenceTransformerLarge Language ModelPrompt EngineeringTextRetrieval-Augmented Generation
🎯 What it does: Training a humanoid robot (PIBOT) using a large language model to generate and execute flight mission plans in real-time based on the pilot's operating handbook (POH), completing aircraft piloting on a full-scale simulator
Flying Robotics Art: ROS-based Drone Draws the Record-Breaking Mural
Andrei A. Korigodskii, Georgii E. Bondar
Robotic IntelligenceSimultaneous Localization and MappingPoint Cloud
🎯 What it does: Designed and deployed an autonomous drone system for creating the world's largest drone mural.
FOCWS: A High Sensitive Flexible Optical Curvature Sensor Inspired by Arthropod Sensory Systems
Jiachen Wei, Yanhong Liu
🎯 What it does: Propose a curvature sensor based on a flexible optical crack waveguide structure (FOCWS), and enhance the angular measurement sensitivity by increasing the optical power loss during bending through cutting the optical core, completing simulation and experimental verification of the optical propagation characteristics and geometric parameters.
FogROS2-FT: Fault Tolerant Cloud Robotics
†. K. Chen, Ken Goldberg
Robotic Intelligence
🎯 What it does: Built the FogROS2-FT multi-cloud fault-tolerant framework, which automatically replicates stateless robot services and routes requests to replicas, returning the first response; supports utilizing cloud computing even when cloud services are unavailable or network QoS is poor, and can safely use low-cost, easily shut-down 'spot' instances.
Foot Arch Stiffness-Based Dynamic Plantar Support Control of Human Walking Gait with Active Pneumatic Insoles
Chenhao Liu, Tao Liu
OptimizationBiomedical Data
🎯 What it does: This study proposes an active pneumatic arch support insole (PASI) combined with dynamic foot sole support control based on arch stiffness, aiming to reduce the metabolic cost of walking.
FootstepNet: an Efficient Actor-Critic Method for Fast On-line Bipedal Footstep Planning and Forecasting
Clément Gaspard, Olivier Ly
Robotic IntelligenceReinforcement Learning
🎯 What it does: Proposed a deep reinforcement learning (Actor-Critic) framework called FootstepNet based on a continuous action space, for rapid online bipedal gait planning in obstacle-filled local environments, with footstep count prediction functionality.
Force and Velocity Prediction in Human-Robot Collaborative Transportation Tasks through Video Retentive Networks
J. E. Domínguez-Vidal, Alberto Sanfeliu
Robotic IntelligenceVideo
🎯 What it does: Studied force/speed prediction for human-robot collaborative object transportation tasks, and extended Retentive Networks to an architecture capable of processing video inputs.
Force-Triggered Control Design for User Intent-Driven Assistive Upper-Limb Robots
Maxime Manzano, Marie Babel
Robotic Intelligence
🎯 What it does: Proposed a force-triggered (FT) controller for upper limb exoskeletons and conducted pre-experiments in pick-and-place tasks;
Formalization of Temporal and Spatial Constraints of Bimanual Manipulation Categories
F. Krebs, Tamim Asfour
🎯 What it does: Based on a bimanual operation taxonomy, a formal description of spatiotemporal constraints for each category was proposed, which was embedded into category-specific controllers to achieve real-time adaptation of bimanual collaborative actions.
Four-Axis Adaptive Fingers Hand for Object Insertion: FAAF Hand
Naoki Fukaya, Kuniyuki Takahashi
Robotic Intelligence
🎯 What it does: Designed and implemented a four-axis adaptive gripper (FAAF Hand) capable of passively adapting in the x, y, z axes, and yaw direction to complete object insertion tasks under positioning errors.
FoveaCam++: Systems-Level Advances for Long Range Multi-Object High-Resolution Tracking
Yuxuan Zhang, Sanjeev J. Koppal
Object TrackingVideo
🎯 What it does: Propose a dual-camera system to achieve high-resolution multi-target tracking up to nearly 1 km and construct a real-time image fusion pipeline;
Fractional Order Modeling and Control of Hydrogel-based Soft Pneumatic Bending Actuators
Jesús De La Morena, Andrés S. Vázquez
Physics RelatedOrdinary Differential Equation
🎯 What it does: This paper identifies the dynamic behavior of soft pneumatic bending actuators (SPBA) through experimental data and models it as a fractional-order model (FOM).
FRAGG-Map: Frustum Accelerated GPU-Based Grid Map
Michele Grimaldi, Pere Ridao Rodriguez
Autonomous DrivingComputational EfficiencySimultaneous Localization and MappingPoint Cloud
🎯 What it does: Proposed a new GPU-accelerated voxel map called FRAGG-Map, which maintains real-time updates even after multiple expansions and efficiently inserts point cloud data through CUDA kernels.
From CAD to URDF: Co-Design of a Jet-Powered Humanoid Robot Including CAD Geometry
Punith Reddy Vanteddu, Daniele Pucci
OptimizationRobotic IntelligenceMesh
🎯 What it does: Proposes a joint optimization framework for simultaneously improving the mechanical design and control performance of the body;
From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control
Yide Shentu, Pieter Abbeel
Robotic IntelligenceTransformerLarge Language ModelSupervised Fine-TuningText
🎯 What it does: Proposes an architecture (LCB) that uses a learnable latent code to bridge the gap between LLM and low-level control strategies, mitigating the limitations of pure language interfaces;
Frontier-Based Exploration for Multi-Robot Rendezvous in Communication-Restricted Unknown Environments
Mauro Tellaroli, Nicola Basilico
Robotic IntelligenceSimultaneous Localization and Mapping
🎯 What it does: Proposes a strategy that unifies frontier exploration with multi-robot rendezvous, introducing a mechanism allowing robots to revisit explored areas to enhance rendezvous opportunities.
Frozen Assets: Leveraging Ice, Water, and Phase Transitions in Robots
Aaron Wilhelm, E. F. Helbling
Robotic Intelligence
🎯 What it does: Demonstrates how the solid and liquid states of water and their phase transitions can be utilized in common robot designs such as modular robots, robotic arms, vehicle-mounted robots, and soft robots.
FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework
Lukas Meyer, Marc Stamminger
Object DetectionSegmentationNeural Radiance FieldImagePoint CloudAgriculture Related
🎯 What it does: We propose the FruitNeRF framework, which directly counts any fruits in 3D space by training a semantic neural radiance field;
Functional kinematic and kinetic requirements of the upper limb during activities of daily living: a recommendation on necessary joint capabilities for prosthetic arms
Christopher Herneth, Sami Haddadin
OptimizationRobotic IntelligenceVideoBiomedical Data
🎯 What it does: Analyzed motion capture data of 12 subjects performing 24 activities of daily living (ADL), calculating the range of motion, velocity, and torque of the shoulder, elbow, radioulnar, and wrist joints; used a linear regression model to predict joint torques under different limb and object masses; optimized the axial configuration of two series and two differential joint configurations of the wrist based on a data-driven approach, achieving reduced peak power.
FusionTrack: An Online 3D Multi-object Tracking Framework Based on Camera-LiDAR Fusion
Weizhen Zeng, Bingzhao Gao
Object TrackingAutonomous DrivingImageMultimodalityPoint CloudBenchmark
🎯 What it does: Proposed an online 3D multi-object tracking framework based on camera-radar fusion, primarily relying on 3D object motion models while fully utilizing image appearance information and 2D detection results.
Gaining the Sparse Rewards by Exploring Lottery Tickets in Spiking Neural Networks
Hao Cheng, Renjing Xu
Computational EfficiencySpiking Neural NetworkTransformer
🎯 What it does: Studied Spike-based Lottery Tickets (SLTs), explored their properties, and achieved two significant sparse rewards through experiments; meanwhile, proposed a sparse algorithm for the Spike Transformer structure, integrating convolutional operations into the Patch Embedding Projection (ConvPEP) module to achieve multi-level sparsification.
GazeMotion: Gaze-guided Human Motion Forecasting
Zhiming Hu, Andreas Bulling
Pose EstimationGraph Neural NetworkMultimodalityBenchmark
🎯 What it does: Proposed the GazeMotion method, which utilizes past eye gaze information and human posture to predict future human motion.
GDM-Net: Gas Distribution Mapping with a Mobile Robot Using Deep Reinforcement Learning and Gaussian Process Regression
Iliya Kulbaka, Swapnoneel Roy
Robotic IntelligenceReinforcement LearningPhysics Related
🎯 What it does: Proposed and implemented a technique combining deep reinforcement learning and Gaussian process regression for gas distribution mapping by mobile robots under limited travel budgets.
GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators
Philipp Wu, P. Abbeel
Robotic Intelligence
🎯 What it does: Built a low-cost, user-friendly, and intuitive teleoperation framework called GELLO for demonstration collection in robotic arm operations, implemented on three common robots (Franka, UR5, xArm).
GenCHiP: Generating Robot Policy Code for High-Precision and Contact-Rich Manipulation Tasks
Kaylee Burns, Karol Hausman
Robotic IntelligenceLarge Language ModelBenchmark
🎯 What it does: Generate robot strategy code using large language models (LLMs), and verify its effectiveness in high-precision and contact-rich manipulation tasks by reparameterizing the action space to include constraints on interaction forces and stiffness.
Generating Continuous Paths On Learned Constraint Manifolds Using Policy Search
Ethan Canzini, Ashutosh Tiwari
OptimizationRobotic IntelligenceReinforcement Learning
🎯 What it does: Train a single learning manifold, incrementally determine constraint manifolds under different constraints using it, and compute the geodesic between two points via policy search to obtain the shortest path.
Generating Force Vectors from Projective Truncated Signed Distance Fields for Collision Avoidance and Haptic Feedback
Seongjin Bien, Sami Haddadin
Depth EstimationRobotic Intelligence
🎯 What it does: Propose a unified system that combines projective truncated signed distance fields (TSDF) with Euclidean signed distance fields (ESDF), generating force vectors near obstacles from TSDF reconstruction and implementing haptic feedback teleoperation based on this.
GenerOcc: Self-supervised Framework of Real-time 3D Occupancy Prediction for Monocular Generic Cameras
Xianghui Pan, Qi Chen
Depth EstimationAutonomous DrivingImage
🎯 What it does: Propose the GenerOcc self-supervised framework to achieve real-time prediction of 3D occupancy status under monocular general cameras, and collect the FishEye Dominant dataset to verify compatibility.
Geolocation on Cartographic Maps with Multi-Modal Fusion
Mengjie Zhou, Andrew Calway
RetrievalAutonomous DrivingSimultaneous Localization and MappingImageMultimodality
🎯 What it does: Proposes upgrading the matching of ground view images with 2D maps to a 2.5D space, and achieving map localization without GPS priors through a method that learns location embeddings via multi-modal feature fusion.
Geometry-aided Underwater 3D Mapping Using Side-scan Sonar
Yiqiao Yang, Zheng Fang
Simultaneous Localization and MappingUltrasound
🎯 What it does: Proposes an underwater 3D mapping framework utilizing side-scan sonar geometry, incorporating feature extraction, binocular line search for non-continuous viewpoints, and landmark localization based on scan matching adjustment.
GeRM: A Generalist Robotic Model with Mixture-of-experts for Quadruped Robot
Wenxuan Song, Donglin Wang
Robotic IntelligenceTransformerReinforcement LearningMixture of ExpertsVision-Language-Action ModelMultimodality
🎯 What it does: Proposed GeRM, a multi-task quadruped robot universal model based on offline reinforcement learning, Transformer VLA network, and Mixture-of-Experts architecture.
GESCE: Graph-based Ergodic Search in Cluttered Environments
Burhanuddin Shirose, Matthew Travers
Autonomous DrivingOptimizationGraph
🎯 What it does: Propose a hybrid motion planning algorithm that first generates a graph of the environment's free space, then uses ergodicity as a heuristic to search the graph, thereby generating paths with low ergodicity that avoid obstacles.
GestRight: Understanding the Feasibility of Gesture-driven Tele-Operation in Human-Robot Teams
Kevin Rippy, Kasthuri Jayarajah
Robotic Intelligence
🎯 What it does: Developed GestRight, a real-time gesture-driven mobile robot remote operating system, which includes a head-mounted device capturing hand joint data and converting gestures into motion commands, with user studies evaluating its effectiveness
GMMCalib: Extrinsic Calibration of LiDAR Sensors using GMM-based Joint Registration
Ilir Tahiraj, Markus Lienkamp
Autonomous DrivingPoint Cloud
🎯 What it does: Proposed GMMCalib, which achieves automatic target benchmark external calibration for multiple LiDAR sensors using GMM-based joint point cloud registration.
GNC Design and Orbital Performance Evaluation of ISS Onboard Autonomous Free-Flying Robot Int-Ball2
Taisei Nishishita, Shinji Mitani
Robotic IntelligenceSimultaneous Localization and MappingPhysics Related
🎯 What it does: Designed the GNC (navigation, attitude control) and propulsion system of Int-Ball2, verified the performance of its 6-DOF translational and rotational motion in orbit, and compared the results of ground microgravity simulation experiments with orbital experimental results.
Goal Estimation-based Adaptive Shared Control for Brain-Machine Interfaces Remote Robot Navigation
Tomoka Muraoka, Takayuki Nagai
Robotic IntelligenceBiomedical Data
🎯 What it does: Using low-frequency, discrete, and noisy user commands generated by a brain-computer interface, the system predicts user intent through target estimation technology and fuses it with high-frequency continuous commands generated by an autonomous system to achieve remote shared control of a mobile robot.
GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment
Lance Ying, Tianmin Shu
Robotic IntelligenceLarge Language ModelAgentic AIText
🎯 What it does: Proposes the Goal-Oriented Mental Alignment (GOMA) framework, which achieves proactive verbal collaborative communication by planning to minimize goal-related errors in the agent's mental state.
Good Things Come in Threes: The Impact of Robot Responsiveness on Workload and Trust in Multi-User Human-Robot Collaboration
Francesco Semeraro (BAE Systems Academy for Skills and Knowledge Centre), Angelo Cangelosi (BAE Systems Academy for Skills and Knowledge Centre)
Robotic Intelligence
🎯 What it does: Designed a physical collaborative experimental scenario where two users work with a collaborative robot to complete manufacturing tasks, and investigated users' perception of task workload and trust in the robot system.
Gradient-based Regularization for Action Smoothness in Robotic Control with Reinforcement Learning
I. Lee, I-Chen Wu
Robotic IntelligenceReinforcement LearningBenchmark
🎯 What it does: Propose and implement Gradient-based CAPS (Grad-CAPS) to reduce jitter in robot control.
Gradual Receptive Expansion Using Vision Transformer for Online 3D Bin Packing
Minjae Kang, Songhwai Oh
OptimizationTransformerReinforcement Learning
🎯 What it does: Proposed a novel reinforcement learning algorithm called GREViT for solving the online 3D bin packing problem, utilizing visual Transformers and progressive receptive field expansion techniques;
Graph Neural Network-based Multi-agent Reinforcement Learning for Resilient Distributed Coordination of Multi-Robot Systems
Anthony Goeckner, Qi Zhu
Robotic IntelligenceGraph Neural NetworkReinforcement Learning
🎯 What it does: Propose a multi-agent reinforcement learning method based on graph neural networks (GNN) for resilient distributed coordination in multi-robot systems.
GraspContrast: Self-supervised Contrastive Learning with False Negative Elimination for 6-DoF Grasp Detection
Wenshuo Wang, M. H. Ang
Pose EstimationRepresentation LearningRobotic IntelligenceContrastive LearningImagePoint Cloud
🎯 What it does: Proposed GraspContrast, a self-supervised contrastive learning framework that leverages unlabeled RGB-D images to enhance point-level feature representations for 6-DoF grasp detection
Grasping Trajectory Optimization with Point Clouds
Yu Xiang, V. Gogate
OptimizationRobotic IntelligencePoint Cloud
🎯 What it does: Proposes a grasp trajectory optimization method based on point cloud representation
Gravity-aware Grasp Generation with Implicit Grasp Mode Selection for Underactuated Hands
Tianyi Ko, Koichi Nishiwaki
Data SynthesisRobotic IntelligencePhysics Related
🎯 What it does: Proposed a data generation and learning process that utilizes gravity resistance scores to train a grasp detector, favoring enclosing grasps
Greedy Perspectives: Multi-Drone View Planning for Collaborative Perception in Cluttered Environments
Krishna Suresh, Sebastian A. Scherer
OptimizationRobotic Intelligence
🎯 What it does: Developed a multi-robot multi-actor viewpoint planner with an occlusion-aware objective for collaborative cinematography in complex environments.
Grid-based Submap Joining: An Efficient Algorithm for Simultaneously Optimizing Global Occupancy Map and Local Submap Frames
Yingyu Wang, Shoudong Huang
OptimizationComputational EfficiencySimultaneous Localization and MappingPoint Cloud
🎯 What it does: Propose a grid-based subgraph stitching method that uses a pose-only Gauss-Newton algorithm to simultaneously optimize the global occupancy grid and local subgraph framework, applicable to large-scale 2D non-feature SLAM.
GRID: Scene-Graph-based Instruction-driven Robotic Task Planning
Zhe-Yuan Ni, Long Zeng
Data SynthesisRobotic IntelligenceGraph Neural NetworkTransformerLarge Language ModelTextGraph
🎯 What it does: Proposed the GRID method, which iteratively generates subtasks using Scene Graphs combined with Large Language Models (LLMs) and Graph Attention Networks (GATs) to achieve instruction-based robot task planning.
GripFlexer: Development of hybrid gripper with a novel shape that can perform in narrow spaces
Donghyun Kim, Dongwon Yun
Robotic Intelligence
🎯 What it does: Developed a compact hybrid gripper called 'GripFlexer,' experimentally verifying its ability to perform complex tasks in confined spaces, particularly challenging tasks such as twisting round door handles in disaster scenarios.
Ground-Density Clustering for Approximate Agricultural Field Segmentation
H. J. Nelson, N. Papanikolopoulos
SegmentationPoint CloudAgriculture Related
🎯 What it does: Propose a new Ground-Density Quickshift++ algorithm for rapid, approximate segmentation of plants and their stems in agricultural field point clouds.
GroupTrack: Multi-Object Tracking by Using Group Motion Patterns
Xinglong Xu, Honghai Liu
Object TrackingVideo
🎯 What it does: Propose a 2D multi-target tracker named GroupTrack that leverages group motion patterns, utilizing combinations of nearby trajectories as prior information for motion prediction and data association.
Grow-to-Shape Control of Variable Length Continuum Robots via Adaptive Visual Servoing
Abhinav Gandhi, B. Çalli
Robotic IntelligenceImage
🎯 What it does: Developed an adaptive eye-hand visual control method that enables a variable-length continuous robot in 2D environments to grow from the shortest length in a closed-loop manner to a preset shape, while maintaining the target shape trajectory.
GS-Planner: A Gaussian-Splatting-based Planning Framework for Active High-Fidelity Reconstruction
Rui Jin, Fei Gao
Robotic IntelligenceGaussian Splatting
🎯 What it does: Propose the GS-Planner framework, which uses 3D Gaussian Splatting to achieve active high-fidelity reconstruction, improves 3DGS to identify unobserved regions and online assess reconstruction quality to guide the robot; design a sampling-based active exploration strategy to enhance geometric and texture quality; adopt a quadrotor robot platform, combine 3DGS safety constraints to generate executable trajectories; validate in a highly realistic simulation environment.
GSLoc: Visual Localization with 3D Gaussian Splatting
Kazii Botashev, Stamatios Lefkimmiatis
Pose EstimationGaussian SplattingImage
🎯 What it does: Proposes a visual localization method called GSLoc based on 3D Gaussian projection, which aligns panoramic cameras using pose gradients in the rendering pipeline and improves convergence with a coarse-to-fine blur kernel strategy;
GSRM: Building Roadmaps for Query-Efficient and Near-Optimal Path Planning Using a Reaction Diffusion System
Christian Henkel, Wolfgang Honig
Autonomous DrivingOptimization
🎯 What it does: Construct a road network based on the Gray-Scott reaction-diffusion system and Delaunay triangulation to enhance the efficiency of mobile robot path planning
Guiding Reinforcement Learning with Incomplete System Dynamics
Shuyuan Wang, Lixian Zhang
Autonomous DrivingReinforcement Learning
🎯 What it does: Propose an embedded control method that utilizes incomplete system dynamics information to guide reinforcement learning.
GV-Bench: Benchmarking Local Feature Matching for Geometric Verification of Long-term Loop Closure Detection
Jingwen Yu, Hong Zhang
Simultaneous Localization and MappingBenchmark
🎯 What it does: Proposed a geometric verification benchmark tailored for long-term loop closure detection, and evaluated six local feature matching methods under this benchmark, deeply analyzing their limitations and future directions.
HabiCrowd: A High Performance Simulator for Crowd-Aware Visual Navigation
Vuong Dinh An, Anh Nguyen
Autonomous DrivingComputational EfficiencyWorld Model
🎯 What it does: Proposed HabiCrowd, a high-performance crowd-aware visual navigation simulation platform that integrates diverse human dynamic models into photorealistic environments;
Haptic Contour Following with the Smart Suction Cup
Sebastian D. Lee, Hannah S. Stuart
Robotic IntelligenceFlow-based Model
🎯 What it does: Studied the contour tracking functionality of Smart Suction Cup, exploring the capability of flow-based tactile sensors to track different speeds and various planar contours.
Hardware-Based Time Synchronization for a Multi-Sensor System
Yueqi Wang, Liao Wu
Autonomous DrivingOptimizationImageTime Series
🎯 What it does: Proposes a hardware-based multi-sensor synchronization scheme, using a Sensor Adaptor board to provide a unified clock reference, and develops a visual-inertial synchronization method that actively controls exposure duration using an ambient light sensor, aligning camera sampling moments with actual IMU sampling moments and significantly reducing time discrepancies.
Hardware-Software Co-Design for Path Planning by Drones
Ayushi Dube, S. Vrudhula
Autonomous DrivingOptimizationRobotic Intelligence
🎯 What it does: Design a hardware-software co-design MT+, applying the Mikami-Tabuchi algorithm to onboard path planning in UAV 3D environments, and develop a dedicated hardware accelerator CDU for parallel collision detection to reduce path planning latency.
Harmonic Mobile Manipulation
Ruihan Yang, Kiana Ehsani
Robotic IntelligenceImageBenchmark
🎯 What it does: Proposes an end-to-end learning method called HarmonicMM, which can simultaneously optimize mobility and manipulation tasks. Its effectiveness is validated in both simulation and real-world environments, demonstrating adaptability to unseen settings without additional tuning, and successfully deployed in real apartments using only RGB visual inputs.
Harnessing Natural Oscillations for High-Speed, Efficient Asymmetrical Locomotion in Quadrupedal Robots
Jing Cheng, Zhenyu Gan
Robotic IntelligenceOrdinary Differential Equation
🎯 What it does: Investigated the dynamics of quadruped robots in asymmetric bounding gait, focusing on integrating trunk pitch and hip movements to enhance speed and stability.
Harnessing Symmetry Breaking in Soft Robotics: A Novel Approach for Underactuated Fingers
Ryman Hashem, Fumiya Iida
Robotic Intelligence
🎯 What it does: Studied a control method that utilizes symmetry breaking in a soft robotic fingertip self-organization mechanism to achieve object rotation within the hand.
Harnessing with Twisting: Single-Arm Deformable Linear Object Manipulation for Industrial Harnessing Task
Xiang Zhang, Masayoshi Tomizuka
Robotic Intelligence
🎯 What it does: Proposed a single-arm wire harnessing production line, generating necessary tension through robotic twisting motions to achieve precise fixture insertion, using only a single-arm robot and an integrated force/torque sensor;
Head-Mounted Hydraulic Needle Driver for Targeted Interventions in Neurosurgery
Zhiwei Fang, Hongliang Ren
Robotic IntelligenceBiomedical DataMagnetic Resonance Imaging
🎯 What it does: Designed and manufactured a 5-DoF head-mounted hydraulic needle driver robot for deep brain needle insertion and neural imaging
Heading Control for Obstacle Avoidance using Dynamic Posture Manipulation during Tumbling Locomotion
Adarsh Salagame (Northeastern University), Alireza Ramezani (Northeastern University)
Robotic Intelligence
🎯 What it does: This paper describes the dynamics of dynamic attitude manipulation during self-rotation motion through a mathematical framework, and verifies the obstacle avoidance capability achieved solely through attitude manipulation in hardware tests.
HeLiMOS: A Dataset for Moving Object Segmentation in 3D Point Clouds From Heterogeneous LiDAR Sensors
Hyungtae Lim, C. Stachniss
SegmentationPoint Cloud
🎯 What it does: Provides a 3D point cloud moving object segmentation label dataset HeLiMOS containing four heterogeneous LiDAR sensors (including two solid-state LiDARs), and proposes an automatic annotation method.
HeteroLight: A General and Efficient Learning Approach for Heterogeneous Traffic Signal Control
Yifeng Zhang, G. Sartoretti
Autonomous DrivingOptimizationTransformerReinforcement LearningGraph
🎯 What it does: Proposes a general traffic signal control framework called HeteroLight based on multi-agent reinforcement learning, utilizing the General Feature Extraction (GFE) module and the Intersection Specifics Extraction (ISE) module to enhance the performance of parameter-sharing models across different intersections;
HGP-RL: Distributed Hierarchical Gaussian Processes for Wi-Fi-based Relative Localization in Multi-Robot Systems
Ehsan Latif, Ramviyas Parasuraman
Computational EfficiencyRobotic Intelligence
🎯 What it does: Propose a distributed relative localization method called HGP-RL based on shared Wi-Fi access points, which models RSSI using hierarchical Gaussian processes to achieve relative localization in multi-robot systems.
Hierarchical Action Chunking Transformer: Learning Temporal Multimodality from Demonstrations with Fast Imitation Behavior
J. H. Park, J. Kwon
Robotic IntelligenceTransformerReinforcement LearningMultimodality
🎯 What it does: Proposed and implemented Hierarchical Action Chunking Transformer with Vector-quantization (HACT-Vq) for learning multimodal trajectories and fine-grained actions from multi-user demonstration data.
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks
Pu Feng, Wenjun Wu
Robotic IntelligenceReinforcement LearningContrastive Learning
🎯 What it does: Proposed the Hierarchical Consensus-based Multi-Agent Reinforcement Learning (HC-MARL) framework, which utilizes contrastive learning to achieve multi-level consensus, constructing global consensus from local observations to guide the execution of multi-robot collaborative tasks.
Hierarchical Large Scale Multirobot Path (Re)Planning
Lishuo Pan, Nora Ayanian
OptimizationRobotic Intelligence
🎯 What it does: Propose a hierarchical multi-robot path re-planning framework that divides the workspace into cells and uses hierarchical planners to achieve real-time re-planning
Hierarchical Search-Based Cooperative Motion Planning
Yuchen Wu, Yong Liu
Autonomous DrivingOptimizationRobotic Intelligence
🎯 What it does: Proposed a leaderless hierarchical search-based cooperative motion planning (SCMP) method for unmanned ground vehicles with nonholonomic Ackermann models;
High Rate Mechanical Coupling of Interacting Objects in the Context of Needle Insertion Simulation With Haptic Feedback
C. Martin, H. Courtecuisse
Computational EfficiencyRobotic Intelligence
🎯 What it does: A high-frequency update flexible coupling contact model is proposed in acupuncture simulation to real-time track needle tip deformation and improve haptic feedback.
High-Accuracy 2-D AoA Estimation Using Lightweight UWB Arrays
Yi Li, Yuan Shen
🎯 What it does: Propose a high-precision 2D azimuth estimation method using a stereo UWB array, and provide a novel phase error calibration and phase ambiguity resolution scheme based on distributed ranging.
High-Fidelity SLAM Using Gaussian Splatting with Rendering-Guided Densification and Regularized Optimization
Shuo Sun, Martin Magnusson
Pose EstimationDepth EstimationGaussian SplattingSimultaneous Localization and MappingImage
🎯 What it does: Proposed a dense RGB-D SLAM system based on 3D Gaussian Splatting, achieving accurate pose tracking and visually realistic reconstruction.
High-Frequency Capacitive Sensing for Electrohydraulic Soft Actuators
Michel R. Vogt, Robert K. Katzschmann
Robotic Intelligence
🎯 What it does: Introduce the F-HASEL motor, adding extra electrode pairs in the Peano-HASEL motor for capacitive self-sensing, demonstrating displacement estimation under high-frequency (up to 20 Hz) excitation and under external loads, proposing a multi-F-HASEL displacement estimation circuit, and enabling real-time tracking of VR user joint rotations in wearable devices.
Highly Efficient Observation Process Based on FFT Filtering for Robot Swarm Collaborative Navigation in Unknown Environments*
Chenxi Li, Bin Liang
Computational EfficiencyRobotic Intelligence
🎯 What it does: Proposed an FFT-based filtering method for extracting safe directions in collaborative path planning for robot swarms in unknown environments, as well as for compressing and reconstructing sensor observation data;
HiLMa-Res: A General Hierarchical Framework via Residual RL for Combining Quadrupedal Locomotion and Manipulation
Xiaoyu Huang, K. Sreenath
Robotic IntelligenceReinforcement Learning
🎯 What it does: Proposed and implemented HiLMa-Res, a generic hierarchical framework for simultaneously achieving continuous walking and manipulation tasks on quadruped robots.
HM3D-OVON: A Dataset and Benchmark for Open-Vocabulary Object Goal Navigation
Naoki Yokoyama, Sehoon Ha
Point CloudBenchmark
🎯 What it does: Proposed the HM3D-OVON dataset and benchmark, and systematically evaluated multiple methods on it