ICRA 2024 Papers — Page 12
IEEE International Conference on Robotics and Automation · 1760 papers
Observer-based Distributed MPC for Collaborative Quadrotor-Quadruped Manipulation of a Cable-Towed Load
Shaohang Xu, Lijun Zhu
OptimizationRobotic Intelligence
🎯 What it does: Propose a collaborative quadrotor-quadruped robot system for manipulating tethered loads, addressing the unknown dynamics of the load by building a new nonlinear dynamic model, designing an observer, and integrating it into a distributed MPC framework.
OCC-VO: Dense Mapping via 3D Occupancy-Based Visual Odometry for Autonomous Driving
Heng Li, Yanyong Zhang
Autonomous DrivingTransformerSimultaneous Localization and MappingImage
🎯 What it does: Propose the OCC-VO framework, which uses deep learning to convert 2D camera images into 3D semantic occupancy grids, achieving visual odometry and global semantic map construction without requiring traditional pose and feature point estimation.
Occluded Part-aware Graph Convolutional Networks for Skeleton-based Action Recognition
Min Hyuk Kim, S. Yoo
RecognitionGraph Neural NetworkAuto EncoderGraph
🎯 What it does: Proposes an Occlusion-Aware Graph Convolutional Network (OP-GCN) for action recognition when human skeletons are occluded
ODD-based Query-time Scenario Mutation Framework for Autonomous Driving Scenario databases
Yun Tang, P. Jennings
Autonomous Driving
🎯 What it does: Propose a query-time scenario mutation framework based on ODD tags to improve query efficiency and diversity in large autonomous driving scenario databases.
Odometry Estimation by Fusing Multiple Radar Sensors and an Inertial Measurement Unit
Tim Brühl, Sören Hohmann
Autonomous DrivingSimultaneous Localization and MappingMultimodalityTime Series
🎯 What it does: Proposes an automotive odometry estimation framework using six asynchronous millimeter-wave radar sensors along with a gyroscope and accelerometer.
Offline Goal-Conditioned Reinforcement Learning for Safety-Critical Tasks with Recovery Policy
Chenyang Cao, Xueqian Wang
Reinforcement Learning
🎯 What it does: Proposes a method called Recovery-based Supervised Learning (RbSL) to address offline goal-conditional reinforcement learning tasks with safety constraints, aiming to achieve multi-objective safety-critical missions.
OmniColor: A Global Camera Pose Optimization Approach of LiDAR-360Camera Fusion for Colorizing Point Clouds
Bonan Liu, Pan Hui
Pose EstimationAutonomous DrivingOptimizationSimultaneous Localization and MappingImageMultimodalityPoint Cloud
🎯 What it does: Propose OmniColor, which utilizes an independent 360-degree camera to jointly optimize camera poses through photometric consistency optimization, mapping images to LiDAR-based point clouds to achieve point cloud coloring.
Omnidirectional Dense SLAM for Back-to-back Fisheye Cameras
Weijian Xie, Guofeng Zhangv
Depth EstimationOptimizationSimultaneous Localization and MappingOptical FlowImageMultimodality
🎯 What it does: Proposes a real-time visual-inertial dense SLAM system, utilizing back-to-back dual fisheye cameras to achieve 360° environmental coverage, incorporating a sliding window frontend and a lightweight panoramic depth completion network based on multi-base depth representation.
OmniLRS: A Photorealistic Simulator for Lunar Robotics
Antoine Richard, Kazuya Yoshida
SegmentationData SynthesisDomain AdaptationData-Centric LearningRobotic IntelligenceConvolutional Neural NetworkImage
🎯 What it does: Propose OmniLRS—a lighting-realistic lunar simulator based on the Nvidia robotics simulator, offering fast procedural environment generation, multi-robot capabilities, synthetic data pipelines for machine learning, and support for ROS1/ROS2 bindings.
On camera model conversions
Eva Goichon, Fumio Kanehiro
Simultaneous Localization and MappingBenchmark
🎯 What it does: Proposes conversion methods between three camera models, enabling the mutual conversion of data calibrated under different projection models, allowing the use of existing data without recalibration;
On Experimental Emulation of Printability and Fleet Aware Generic Mesh Decomposition for Enabling Aerial 3D Printing
Marios-Nektarios Stamatopoulos, G. Nikolakopoulos
OptimizationRobotic IntelligenceMeshPhysics Related
🎯 What it does: The feasibility of a block-based multi-degree-of-freedom aerial drone 3D printing framework was verified through experimental simulation, demonstrating precise motion planning and task allocation.
On Robust Control Laws Trade-off Analysis for Space Manipulators with Uncertain Parameters and Flexible Appendages*
Kostas Nanos, E. Papadopoulos
Robotic IntelligencePhysics Related
🎯 What it does: Studied and compared robust controllers for space manipulators under parameter uncertainty, noisy measurements, and disturbances, with a focus on evaluating the performance of LPV+H∞ controllers against model-based PD controllers and PD+H∞ combinations.
On the Disentanglement of Tube Inequalities in Concentric Tube Continuum Robots
R. Grassmann, J. Burgner-Kahrs
Robotic Intelligence
🎯 What it does: A mapping method was studied to alleviate the discontinuity issues caused by branch methods (e.g., if-else statements) in continuum robots, deriving a lower triangular transformation matrix to decouple inequalities, converting interdependent inequalities into independent box constraints, and further investigating sampling, control, and workspace estimation.
On the Feasibility of EEG-based Motor Intention Detection for Real-Time Robot Assistive Control
Ho Jin Choi, Nadia Figueroa
ClassificationRobotic IntelligenceBiomedical Data
🎯 What it does: This study designs an offline data collection and training process as well as an online real-time prediction workflow, utilizing EEG signals to classify the intention of left and right arm movements, and applying the prediction results to assistive robot control;
On the Fly Robotic-Assisted Medical Instrument Planning and Execution Using Mixed Reality
Letian Ai, Alejandro Martin-Gomez
Robotic IntelligenceBiomedical Data
🎯 What it does: Propose a framework based on mixed reality that supports real-time planning and execution in robot-assisted medical systems, combining 3D anatomical image overlay, human-robot collision detection, and robot programming interfaces, along with an intuitive head-mounted display (HMD) calibration method to enhance human-robot interaction.
On the Overconfidence Problem in Semantic 3D Mapping
João Marcos Correia Marques, Kris Hauser
SegmentationDepth EstimationSimultaneous Localization and MappingPoint Cloud
🎯 What it does: Investigated the overconfidence in fusion within semantic 3D mapping, proposed improved uncertainty calibration methods, and conducted comparisons on ScanNet.
On the Performance of Jerk-Constrained Time-Optimal Trajectory Planning for Industrial Manipulators
Jee-eun Lee, Luis Sentis
OptimizationRobotic Intelligence
🎯 What it does: Propose a jerk-constrained time-optimal trajectory planning method, which forms conservative inequality constraints by convexifying third-order constraints, and uses n-dimensional sequential linear programming (SLP) to iteratively solve for the trajectory, followed by evaluating its performance on a real robot;
On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors
Elia Cereda, D. Palossi
Domain AdaptationConvolutional Neural NetworkImage
🎯 What it does: Performing self-supervised fine-tuning of pre-trained CNNs on sub-50g nano-drones to enhance performance in visual perception regression tasks.
On-the-Go Tree Detection and Geometric Traits Estimation with Ground Mobile Robots in Fruit Tree Groves
Dimitrios Chatziparaschis, Konstantinos Karydis
Object DetectionImageMultimodalityPoint CloudAgriculture Related
🎯 What it does: Propose an algorithm framework for real-time fruit tree detection and estimation of key geometric features such as width and height during vehicle movement, combining NDVI 2D images and 3D LiDAR point clouds, and achieving multi-modal feature matching and joint estimation with the Kalman filter through tree feature point association and parameter estimation algorithms.
One-vs-All Semi-Automatic Labeling Tool for Semantic Segmentation in Autonomous Driving
Jing Gu, Amine Ben Arab
SegmentationAutonomous DrivingImage
🎯 What it does: Proposed an active learning framework called OVAAL based on One-vs-All, as well as an OVA-based semi-supervised learning method called OVAAL+, which were evaluated on semantic segmentation tasks in autonomous driving scenarios.
ONeK-SLAM: A Robust Object-level Dense SLAM Based on Joint Neural Radiance Fields and Keypoints
Yue Zhuge, Zhuqing Jiang
Neural Radiance FieldSimultaneous Localization and Mapping
🎯 What it does: Proposed a robust dense SLAM system called ONeK-SLAM based on the joint use of object-level neural radiance fields and keypoints.
Online Adaptation of Sampling-Based Motion Planning with Inaccurate Models
Marco Faroni, Dmitry Berenson
OptimizationRobotic Intelligence
🎯 What it does: Proposed an online adaptive sampling-based motion planning method, utilizing model error estimation and online observations to correct the planning strategy.
Online Calibration of a Single-Track Ground Vehicle Dynamics Model by Tight Fusion with Visual-Inertial Odometry
Haolong Li, Joerg Stueckler
Autonomous DrivingOptimizationSimultaneous Localization and Mapping
🎯 What it does: Integrate a single-track dynamic model with visual-inertial odometry (VIO), and perform online calibration and model adaptation to enhance the accuracy of forward predictions for future control inputs.
Online Data-Driven Safety Certification for Systems Subject to Unknown Disturbances
Nicholas Rober, Jonathan P. How
Optimization
🎯 What it does: Developed an online data-driven security authentication method that utilizes an optimized moving horizon estimator (MHE) to estimate unknown disturbances, and integrates the estimation results into real-time reachability analysis to verify whether the system avoids hazardous states under unknown disturbances.
Online Distribution Shift Detection via Recency Prediction
Rachel Luo, M. Pavone
Anomaly DetectionRobotic IntelligenceTime SeriesSequential
🎯 What it does: Proposed an online distribution drift detection method using recency prediction technology, achieving rapid detection in high-dimensional robot data streams with a theoretical guarantee of false alarm rate < ε.
Online Estimation of Articulated Objects with Factor Graphs using Vision and Proprioceptive Sensing
Russell Buchanan, S. Vijayakumar
Pose EstimationRobotic IntelligenceSimultaneous Localization and MappingMultimodality
🎯 What it does: Propose an online estimation method that combines availability information predicted by visual neural networks with interactive kinematic sensing to identify joint objects and enable robotic autonomous opening.
Online Fault Detection in Manipulation Tasks via Generative Models
Michael W. Lanighan, Oscar Youngquist
Anomaly DetectionRobotic IntelligenceGenerative Adversarial NetworkImage
🎯 What it does: Construct an image manifold containing only correctly executed images using generative adversarial networks (GAN), and achieve online error detection in manipulation tasks by leveraging differences in image reconstruction.
Online Minimization of the Robot Silhouette Viewed From Eye-to-Hand Camera
Giovanni Cortigiani, Domenico Prattichizzo
OptimizationRobotic IntelligenceImage
🎯 What it does: A control technique was developed using redundant robots from the eye-hand camera perspective to minimize the robot's silhouette in images.
Online Multi-Contact Feedback Model Predictive Control for Interactive Robotic Tasks
Seohee Han, Min Jun Kim
Robotic Intelligence
🎯 What it does: Propose an online multi-contact feedback model predictive control (MPC) for handling multi-contact interactive robotic tasks with unknown contact positions.
Online On-Demand Multi-Robot Coverage Path Planning
Ratijit Mitra, I. Saha
Robotic IntelligenceBenchmark
🎯 What it does: Proposed an online centralized multi-robot coverage path planning algorithm based on time-domain synchronization, which recalculates paths only for robots that have fully traversed their paths on demand, while other robots reuse existing paths, significantly reducing computational burden.
Online Supervised Training of Spaceborne Vision during Proximity Operations using Adaptive Kalman Filtering
T. Park, Simone D’Amico
Pose EstimationDomain AdaptationImage
🎯 What it does: Developed an online supervised training (OST) method that utilizes an adaptive unscented Kalman filter to provide pseudo labels for a pose estimation neural network, enabling online learning with real-time aerial imagery during close-range rendezvous and proximity operations (RPO).
Online Trajectory Deformation and Tracking for Self-entanglement-free Differential-Driven Robots
Jiangpin Liu, Rong Xiong
OptimizationRobotic Intelligence
🎯 What it does: Designed and implemented an optimization-based trajectory deformation and tracking algorithm for achieving self-entanglement free (SEF) tethered differential drive robots;
Online-Learning-Based Distributionally Robust Motion Control with Collision Avoidance for Mobile Robots
Han Wang, Weidong Zhang
OptimizationRobotic Intelligence
🎯 What it does: A distributionally robust nonlinear model predictive control (NMPC) method based on online learning is proposed to achieve collision avoidance in environments with moving obstacles.
Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0
A. Padalkar, Zichen Jeff Cui
Robotic Intelligence
🎯 What it does: A standardized dataset containing 22 robots, 527 skills, and 160,266 tasks was constructed, and a large-scale model named RT-X was trained to demonstrate positive transfer and performance improvement in general robotic policies.
Open-Fusion: Real-time Open-Vocabulary 3D Mapping and Queryable Scene Representation
Kashu Yamazaki (University of Arkansas), Ngan Le (University of Arkansas)
RecognitionSegmentationVision Language ModelMultimodalityPoint Cloud
🎯 What it does: Propose a real-time open-vocabulary 3D mapping and queryable scene representation method, using RGB-D data to achieve annotation-free 3D segmentation and open-vocabulary querying;
Open-Vocabulary Affordance Detection using Knowledge Distillation and Text-Point Correlation
Tuan V. Vo, Anh Nguyen
Knowledge DistillationPoint Cloud
🎯 What it does: Propose an open-vocabulary 3D point cloud object detection method based on knowledge distillation and text-point correlation.
OpenAnnotate3D: Open-Vocabulary Auto-Labeling System for Multi-modal 3D Data
Yijie Zhou, Wenchao Ding
Object DetectionSegmentationTransformerLarge Language ModelVision Language ModelMultimodalityChain-of-Thought
🎯 What it does: Proposed and implemented an open-source multi-modal 3D auto-annotation system named OpenAnnotate3D, which can automatically generate 2D masks, 3D masks, and 3D bounding boxes;
OpenBot-Fleet: A System for Collective Learning with Real Robots
Matthias A. M¨uller, V. Koltun
Robotic Intelligence
🎯 What it does: Introduces the OpenBot-Fleet system, which utilizes smartphone sensors, cloud storage, and low-cost wheeled robots to enable large-scale mobile robots to collect data, learn, and deploy navigation strategies in real-world environments.
Opportunistic Communication in Robot Teams
Daniel Mox, Vijay Kumar
OptimizationRobotic Intelligence
🎯 What it does: Propose a Mobile Infrastructure on Demand (MID) method that employs a dedicated team of robots to construct and maintain a wireless network, meeting the communication needs of another set of task robots in the absence of existing communication infrastructure.
Optimal Collaborative Transportation for Under-Capacitated Vehicle Routing Problems using Aerial Drone Swarms
Akash Kopparam Sreedhara, Heinz Koeppl
Optimization
🎯 What it does: Studied a model for collaborative transportation using drone swarms, defining it as a new 'under-capacity' vehicle routing problem (VRP)
Optimal Containment Control of Multiple Quadrotors via Reinforcement Learning*
Ming Cheng, Xiangke Wang
OptimizationReinforcement Learning
🎯 What it does: Explores the optimal containment control problem for nonlinear, underactuated quadrotors in multi-leader scenarios, proposing a cascade controller using reinforcement learning and deriving control protocols from historical data without requiring an exact dynamic model.
Optimal Control for Clutched-Elastic Robots: A Contact-Implicit Approach
Dennis Ossadnik, Sami Haddadin
OptimizationRobotic Intelligence
🎯 What it does: This paper proposes a contact-implicit optimization scheme that simultaneously optimizes the control inputs and clutch sequences of elastic robots to achieve precise energy transfer timing control.
Optimal Control of Granular Material
Yuichiro Aoyama, Evangelos A. Theodorou
OptimizationGraph Neural NetworkGraphPhysics Related
🎯 What it does: This paper addresses granular material systems driven by rigid bodies, using a graph neural network-based simulator to learn their dynamics, and achieving optimal control through differential dynamic programming to form target shapes.
Optimal Control Synthesis with Relaxed Global Temporal Logic Specifications for Homogeneous Multi-robot Teams
Disha Kamale, C. Vasile
OptimizationRobotic Intelligence
🎯 What it does: Control synthesis for homogeneous robot teams under given global temporal logic specifications and relaxed user preferences when feasibility fails.
Optimal Driver Warning Generation in Dynamic Driving Environment
Chenran Li, Behzad Dariush
Autonomous DrivingOptimizationReinforcement Learning
🎯 What it does: Studied the optimal driver warning generation problem in dynamic driving environments, considering the interaction between warnings, driver behavior, and vehicle states, and modeled it as a partially observable Markov decision process (POMDP)
Optimal Planning for Timed Partial Order Specifications
Kandai Watanabe, S. Sankaranarayanan
OptimizationRobotic Intelligence
🎯 What it does: Proposes a framework for multi-robot task planning aimed at minimizing the total makespan while satisfying temporal and precedence constraints.
Optimal Prescribed-Time Control based Reactive Planning System for Quadruped Robot Navigation
Shaohang Xu, Lijun Zhu
OptimizationRobotic Intelligence
🎯 What it does: Propose a reactive planning system for quadruped robots based on preset-time control, enabling omni-directional movement and correcting deviations through feedback control.
Optimal Scene Graph Planning with Large Language Model Guidance
Zhirui Dai, Nikolay Atanasov
OptimizationTransformerLarge Language ModelText
🎯 What it does: Developed an efficient task planning algorithm for hierarchical metric-semantic models, leveraging large language models (LLMs) to convert natural language tasks into LTL automata, achieving optimal hierarchical LTL planning on scene graphs.
Optimal Task Allocation for Heterogeneous Multi-robot Teams with Battery Constraints
Alvaro Calvo, Jesús Capitán
OptimizationRobotic Intelligence
🎯 What it does: Propose a task allocation method for heterogeneous multi-robot teams, considering battery constraints, task fragmentation, and synchronization in multi-robot collaboration.
Optimization and Evaluation of a Multi Robot Surface Inspection Task Through Particle Swarm Optimization
Darren Chiu, Bahar Haghighat
ClassificationOptimizationRobotic Intelligence
🎯 What it does: Studied a simplified two-class surface inspection task, using a 3 cm wheeled robot swarm to perform distributed Bayesian decision classification on a 1m×1m random black-and-white tile surface, with algorithm parameters adjusted via particle swarm optimization.
Optimization Based Dynamic Skateboarding of Quadrupedal Robot
Zhe Xu, Shiwu Zhang
OptimizationRobotic Intelligence
🎯 What it does: Developed an optimization-based control pipeline enabling the quadruped robot CyberDog2 to achieve dynamic balance and acceleration on a skateboard;
Optimization of Flexible Bronchoscopy Shape Sensing Using Fiber Optic Sensors
Xinran Liu, Hongbin Liu
OptimizationBiomedical Data
🎯 What it does: Proposed a shape assessment and optimization method for flexible bronchoscopes to handle complex spatial configurations in the human bronchial tree.
Optimized Design and Fabrication of Skeletal Muscle Actuators for Bio-syncretic Robots
Lianchao Yang, Lianqing Liu
OptimizationRobotic Intelligence
🎯 What it does: An optimized design method was proposed, combining simulation analysis with experimental preparation to fabricate multi-band structured engineered skeletal muscle tissue (eSKT), which was applied to bio-synthetic robots, achieving significant contractile force and driving functionality.
Optimizing Dynamic Balance in a Rat Robot via the Lateral Flexion of a Soft Actuated Spine
Yuhong Huang, A. Knoll
OptimizationRobotic Intelligence
🎯 What it does: Developed an optimized controller for soft elastic scoliosis to enhance dynamic balance in an experimental mouse robot during stepping locomotion, achieving balance optimization without altering leg movements.
Optimizing Modular Robot Composition: A Lexicographic Genetic Algorithm Approach
Jonathan Külz, Matthias Althoff
OptimizationRobotic Intelligence
🎯 What it does: Propose a modular robot construction optimization method combining genetic algorithms with ordinal evaluation
Optimizing Multi-Touch Textile and Tactile Skin Sensing Through Circuit Parameter Estimation
Boquan Su, Changliu Liu
Optimization
🎯 What it does: A new framework is proposed that treats the touch perception problem as a resistance sensing array estimation problem, utilizing a regularized least squares objective function to enhance touch perception accuracy and reduce ghosting effects.
OptiState: State Estimation of Legged Robots using Gated Networks with Transformer-based Vision and Kalman Filtering
Alexander Schperberg, Dennis Hong
OptimizationRobotic IntelligenceRecurrent Neural NetworkTransformerAuto EncoderSimultaneous Localization and MappingImage
🎯 What it does: A hybrid method is proposed, combining Kalman filtering, convex MPC optimization, and learning techniques. It utilizes joint encoders, IMU, and ground reaction force control outputs, and employs GRU gated networks and Vision Transformer autoencoders to perform semantic and height reasoning on depth images, achieving precise estimation of the quadruped robot's trunk state.
Orbit-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity
Qinxi Yu, Animesh Garg
Robotic IntelligenceReinforcement LearningBenchmark
🎯 What it does: This paper proposes Orbit-Surgical, a physics-based surgical robot simulation framework with realistic rendering from NVIDIA Omniverse, providing 14 benchmark surgical tasks for da Vinci Research Kit (dVRK) and Smart Tissue Autonomous Robot (STAR), and leveraging GPU parallelization to train reinforcement learning and imitation learning algorithms, enabling robot learning to assist human surgical skills, and demonstrating policy transfer from simulation to real-world environments.
Orientation-Aware Multi-Modal Learning for Road Intersection Identification and Mapping
Qibin He, Li Sun
Object DetectionAutonomous DrivingMultimodalityPoint CloudSequential
🎯 What it does: Proposed a direction-oriented multi-modal learning paradigm, formulating the intersection recognition task as a directed object detection problem.
OriTrack: A Small, 3 Degree-of-Freedom, Origami Solar Tracker
Crystal E. Winston, L. Casey
🎯 What it does: Design, manufacture, and control a small 3-degree-of-freedom origami solar tracker named OriTrack
OSCaR: An Origami-Inspired Shape-Changing Robot for Ground Coverage Tasks
Zirui Fan, Hongying Zhang
Robotic Intelligence
🎯 What it does: Developed a foldable and deployable robot called OSCaR to enhance adaptability in ground coverage tasks such as floor cleaning.
Osiris: Building Hierarchical Representations for Agricultural Environments
Adam Mukuddem, Paul Amayo
Representation LearningPoint CloudGraphAgriculture Related
🎯 What it does: Built a 3D scene graph generation system called Osiris for agricultural environments, capable of incrementally generating hierarchical graph representations from data collected by mobile robots.
Ospreys-inspired Self-takeoff Strategy of An Eagle-scale Flapping-wing Robot: System Design and Flight Experiments
Haoyu Wang, Erzhen Pan
Robotic Intelligence
🎯 What it does: First achievement of autonomous takeoff for a hawk-level flapping-wing robot, proposing a bio-inspired takeoff strategy inspired by pelicans, and designing a system comprising a flapping-wing robot and an auxiliary platform, conducting flight experiments under different thrust-to-weight ratios and takeoff angles.
OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted Surgery
Long Bai, Hongliang Ren
RecognitionRobotic IntelligenceVideoBiomedical Data
🎯 What it does: Proposed an open-set surgical activity recognition framework (OSSAR) for robot-assisted surgery
Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects with Video Tracking Enabled Memory Models
Yixuan Huang, Tucker Hermans
Object TrackingRobotic IntelligenceTransformerWorld ModelPoint Cloud
🎯 What it does: Proposed two models, DOOM and LOOM, leveraging transformer relational dynamics and video tracking enhanced memory models to encode memory for occluded objects in multi-object manipulation by robots, supporting reasoning and planning.
Output-Sampled Model Predictive Path Integral Control (o-MPPI) for Increased Efficiency
Leon Yan, S. Devasia
Autonomous DrivingOptimizationReinforcement Learning
🎯 What it does: Developed o-MPPI, an output-sampling-based MPPI, to improve sampling efficiency in meeting output constraints under dynamic environments
Outram: One-shot Global Localization via Triangulated Scene Graph and Global Outlier Pruning
Peng Yin, Lihua Xie
Pose EstimationRetrievalAutonomous DrivingSimultaneous Localization and MappingPoint Cloud
🎯 What it does: Propose a hierarchical one-time LiDAR localization algorithm called Outram, which utilizes substructures of 3D scene graphs for local consistency correspondence search and global substructure-level outlier removal, combining feature retrieval and correspondence extraction to achieve consistency refinement from local to global, addressing substructure ambiguity issues.
Overcoming Hand and Arm Occlusion in Human-to-Robot Handovers: Predicting Safe Poses with a Multimodal DNN Regression Model
C. Lollett, Shigeki Sugano
Pose EstimationRobotic IntelligenceMultimodality
🎯 What it does: A three-branch multimodal DNN regression model was developed to predict safe robotic arm poses under occlusion conditions.
Overparametrization helps offline-to-online generalization of closed-loop control from pixels
Mathias Lechner, Daniela Rus
Robotic IntelligenceConvolutional Neural NetworkTransformerReinforcement LearningImage
🎯 What it does: Investigate the generalization ability of visual models from open training to closed-loop deployment, evaluating their closed-loop test performance in both in-distribution and out-of-distribution scenarios.
PanNote: an Automatic Tool for Panoramic Image Annotation of People’s Positions
Alberto Bacchin, Emanuele Menegatti
Object DetectionConvolutional Neural NetworkImagePoint Cloud
🎯 What it does: Developed and validated a tool called PanNote for automatic annotation of human positions in panoramic videos
Parallel Optimization with Hard Safety Constraints for Cooperative Planning of Connected Autonomous Vehicles
Zhen Huang, Jun Ma
Autonomous DrivingOptimization
🎯 What it does: Formulate cooperative autonomous driving tasks as an optimal control problem with hard safety constraints, propose a computationally efficient parallel optimization framework, maintain problem convexity through convex approximation, and use iterative nearest neighbor search to determine the optimal passage order;
Parameter-efficient Prompt Learning for 3D Point Cloud Understanding
Hongyu Sun, Deying Li
ClassificationSegmentationComputational EfficiencyTransformerPrompt EngineeringPoint Cloud
🎯 What it does: Proposes a parameter-efficient Prompt tuning method called PPT to adapt large multimodal models for 3D point cloud understanding tasks.
Partial Belief Space Planning for Scaling Stochastic Dynamic Games
K. Vakil, Alyssa Pierson
Computational EfficiencyReinforcement Learning
🎯 What it does: Propose a method that reduces the computational burden in stochastic dynamic games by utilizing partial belief propagation
Particle Filter with Stable Embedding for State Estimation of the Rigid Body Attitude System on the Set of Unit Quaternions
Hee-Deok Jang, D. Chang
Pose EstimationComputational Efficiency
🎯 What it does: Proposes a particle filter (Particle Filter) combined with a stable embedding method for state estimation of rigid body attitude systems on the unit quaternion (S³) manifold.
Passive Aligning Physical Interaction of Fully-Actuated Aerial Vehicles for Pushing Tasks
Tong Hui, R. Siegwart
Robotic Intelligence
🎯 What it does: Proposed a control strategy for fully actuated UAVs to achieve passive alignment during pushing tasks
PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning
Wenhao Yu, Jianmin Ji
Autonomous DrivingReinforcement Learning
🎯 What it does: Proposes PathRL, a method that directly generates navigation paths through deep reinforcement learning by training strategies.
Pay Attention to How You Drive: Safe and Adaptive Model-Based Reinforcement Learning for Off-Road Driving
Sean J. Wang, Aaron M. Johnson
Autonomous DrivingTransformerReinforcement Learning
🎯 What it does: This paper proposes a model-based reinforcement learning method for off-road driving, training a System Identification Transformer (SIT) and an Adaptive Dynamics Model (ADM), and using an online risk-aware model with a path integral controller to achieve safe control.
PBP: Path-based Trajectory Prediction for Autonomous Driving
S. Afshar, Henggang Cui
Autonomous DrivingSequential
🎯 What it does: Proposed and implemented a path-based trajectory prediction model (PBP), which performs discrete probability prediction on the reference path distribution in HD maps and generates trajectories in the relative Frenet coordinate system;
PCB-RandNet: Rethinking Random Sampling for LiDAR Semantic Segmentation in Autonomous Driving Scene
Hui Cheng, Guoqiang Xiao
SegmentationAutonomous DrivingPoint Cloud
🎯 What it does: Proposes Polar Cylinder Balanced Random Sampling (PCB-RandNet) and introduces a sampling consistency loss for semantic segmentation of large-scale LiDAR point clouds.
Pedestrian Trajectory Prediction Using Dynamics-based Deep Learning
Honghui Wang, Rohitash Chandra
Object TrackingTransformerSequentialBenchmarkOrdinary Differential Equation
🎯 What it does: Proposed a dynamics-based deep learning framework that integrates an asymptotically stable dynamics system into the Transformer model for pedestrian trajectory prediction.
Pedipulate: Enabling Manipulation Skills using a Quadruped Robot’s Leg
Philip Arm, Marco Hutter
Robotic IntelligenceReinforcement Learning
🎯 What it does: Achieve leg-based single-foot position tracking through training reinforcement learning strategies, developing a walkable-manipulable controller capable of performing manipulation tasks within a large workspace. Real-world experiments on quadruped robots demonstrate successful completion of tasks such as door opening, sampling, and obstacle pushing, with the ability to carry loads exceeding 2.0 kg.
Pegasus: a Novel Bio-inspired Quadruped Robot with Underactuated Wheeled-Legged Mechanism *
Yuzhen Pan, Huiliang Shang
OptimizationRobotic Intelligence
🎯 What it does: Designed and analyzed the Pegasus quadruped wheeled robot, providing two motion modes and achieving the ability to adapt to different tasks.
PeLiCal: Targetless Extrinsic Calibration via Penetrating Lines for RGB-D Cameras with Limited Co-visibility
Jaeho Shin, Ayoung Kim
Pose EstimationDepth EstimationImage
🎯 What it does: Proposes PeLiCal, a target-free, real-time, and robust RGB-D camera extrinsic calibration method based on long straight line features in the scene.
Perception through Cognitive Emulation : "A Second Iteration of NaivPhys4RP for Learningless and Safe Recognition and 6D-Pose Estimation of (Transparent) Objects"
Franklin Kenghagho Kenfack, Michael Beetz
RecognitionPose EstimationSafty and PrivacyPhysics Related
🎯 What it does: Designed and implemented the first version of NaivPhys4RP, which can achieve learning-free and risk-free object recognition and 6D pose estimation under challenging sensor conditions such as data scarcity, occlusion, transparency, low-quality depth, or handheld scenarios.
Perception-and-Energy-aware Motion Planning for UAV using Learning-based Model under Heteroscedastic Uncertainty
Reiya Takemura, G. Ishigami
OptimizationRobotic IntelligencePoint Cloud
🎯 What it does: Proposes a UAV motion planning method under GNSS-restricted environments, combining energy consumption and LiDAR perception quality, achieving efficient and reliable flight through cost function optimization.
Perceptual Factors for Environmental Modeling in Robotic Active Perception
David Morilla-Cabello, Eduardo Montijano
Robotic IntelligenceGraph Neural NetworkSimultaneous Localization and MappingWorld ModelImage
🎯 What it does: Proposed a novel active perception framework that integrates perceptual factors into planning and fusion, quantifying the environmental impact on candidate perspectives through constructing a perceptual graph, and adjusting measurement uncertainty accordingly.
Persistent Monitoring of Large Environments with Robot Deployment Scheduling in between Remote Sensing Cycles
Kizito Masaba, Alberto Quattrini Li
OptimizationRobotic Intelligence
🎯 What it does: Studied the deployment planning of autonomous surface vehicles (ASVs) based on prior data for time and location to continuously monitor spatiotemporal phenomena in environments such as large lakes.
Personality- and Memory-Based Software Framework for Human-Robot Interaction
Alice Nardelli, C. Recchiuto
Robotic Intelligence
🎯 What it does: Proposed a psychological cognitive architecture based on robot personality and memory for human-robot interaction
Phase Synthesis for Spatial Locomotion Control of Retractable Worm Robots
Zhongcheng Wang, Bin Liang
Robotic Intelligence
🎯 What it does: A phase synthesis scheme is proposed for motion control of a scalable worm robot in three-dimensional space, and based on this scheme, the RW-Robot prototype was developed. The foot phase is defined, motion is divided into short-term and long-term, and a frequency domain compression mode is applied, ultimately generating a new spatial gait. The effectiveness of the scheme in turning and climbing was verified through actual experiments.
Phasic Diversity Optimization for Population-Based Reinforcement Learning
Jing Jiang, Xin Yang
OptimizationReinforcement Learning
🎯 What it does: Proposed and implemented the Phasic Diversity Optimization (PDO) algorithm, which separates different stages in reward and diversity training, and employs diversity enhancement in the auxiliary phase without affecting performance.
Physical and Digital Adversarial Attacks on Grasp Quality Networks
N. Alharthi, Martim Brandão
Adversarial AttackRobotic Intelligence
🎯 What it does: Proposed and experimentally tested two physical and digital adversarial attacks targeting grasp quality networks
Physical Priors Augmented Event-Based 3D Reconstruction
Jiaxu Wang, Renjing Xu
GenerationNeural Radiance FieldPhysics Related
🎯 What it does: Propose to enhance NeRF training by leveraging motion, geometry, and density priors, and introduce a density-guided patch sampling strategy that enables direct 3D scene reconstruction from event streams, while constructing the first large-scale event-based 3D reconstruction dataset.
Physically Grounded Vision-Language Models for Robotic Manipulation
Jensen Gao, Dorsa Sadigh
Robotic IntelligenceLarge Language ModelSupervised Fine-TuningVision Language ModelImageTextMultimodality
🎯 What it does: Create the PHYSOBJECTS dataset, fine-tune a vision-language model using it, integrate it with a robot planner based on a large language model, and evaluate on both simulated and real robots.
Physics-Encoded Graph Neural Networks for Deformation Prediction under Contact
Mahdi Saleh, Federico Tombari
Graph Neural NetworkGraphPhysics Related
🎯 What it does: Using Physics-Encoded Graph Neural Networks to predict deformation under contact between rigid and deformable bodies.
Physics-informed Neural Motion Planning on Constraint Manifolds
Ruiqi Ni, A. H. Qureshi
OptimizationComputational EfficiencyRobotic IntelligencePhysics Related
🎯 What it does: Proposed and implemented the first physics-informed CMP framework for solving the Eikonal equation on constrained manifolds, which uses neural networks to train motion planning functions without requiring expert data.
Physics-Informed Neural Network for Multirotor Slung Load Systems Modeling
Gil Serrano, Rita Cunha
Robotic IntelligenceRecurrent Neural NetworkPhysics Related
🎯 What it does: Using physics-informed neural networks for end-to-end modeling of a multirotor suspended load system and predicting future state sequences.
Physics-Informed Neural Networks for Continuum Robots: Towards Fast Approximation of Static Cosserat Rod Theory
Martin Bensch, Moritz Schappler
Computational EfficiencyRobotic IntelligencePhysics Related
🎯 What it does: Compute the overall shape of tension-driven continuum robots using physics-informed neural networks (PINN) based on Cosserat rod theory.
PillarGen: Enhancing Radar Point Cloud Density and Quality via Pillar-based Point Generation Network
Ji Song Kim, J. Choi
Object DetectionGenerationData SynthesisAutonomous DrivingPoint Cloud
🎯 What it does: Transfer radar point clouds from one domain to another using the PillarGen model, generating denser and higher quality point clouds.
Pilot comparison of customized and generalized hip-knee-ankle exoskeleton torque profiles
Gwendolyn M. Bryan, S. Collins
OptimizationRobotic Intelligence
🎯 What it does: Compare the effects of personalized, other, and average torque profiles on metabolic cost using previously optimized hip-knee-ankle torque profiles in three expert users.
PlaceNav: Topological Navigation through Place Recognition
Lauri Suomela, Joni-Kristian Kämäräinen
RecognitionRetrievalRobotic IntelligenceImage
🎯 What it does: Propose PlaceNav, which decomposes robot-agnostic components into navigation-specific and general computer vision modules, leveraging visual place recognition for subgoal selection and Bayesian filtering to enhance subgoal temporal consistency.