IROS 2023 Papers — Page 9
IEEE/RSJ International Conference on Intelligent Robots and Systems · 1195 papers
Open-Vocabulary Affordance Detection in 3D Point Clouds
Toan Ngyen, A. Nguyen
Object DetectionVision-Language-Action ModelPoint Cloud
🎯 What it does: Propose an Open-Vocabulary Affordance Detection (OpenAD) method that can detect an infinite number of affordances in 3D point clouds.
Optical Flow Boosts Unsupervised Localization and Segmentation
Xinyu Zhang, Abdeslam Boularias
Object DetectionSegmentationTransformerContrastive LearningOptical Flow
🎯 What it does: Fine-tune an unsupervised vision transformer (ViT) using optical flow information to enhance performance in unsupervised object localization and segmentation.
Optimal and Stable Multi-Layer Object Rearrangement on a Tabletop
Andy Xu, Jingjin Yu
OptimizationRobotic Intelligence
🎯 What it does: This paper investigates the multi-layer desktop object rearrangement (MORT) problem and proposes an optimal and stable rearrangement algorithm;
Optimal Cost-Preference Trade-Off Planning with Multiple Temporal Tasks
Peter Amorese, Morteza Lahijanian
Optimization
🎯 What it does: Proposed a new preference concept and constructed a Pareto compromise planning framework for multi-task scenarios with optimal behavior of preferences and resources, and implemented efficient Pareto-optimal plan generation based on A* search;
Optimal Decision Making in Robotic Assembly and Other Trial-and-Error Tasks
J. Watson, N. Correll
OptimizationRobotic IntelligenceConvolutional Neural Network
🎯 What it does: Studied robot tasks under uncertainty in perception, execution, and environment by constructing low-entropy success/failure indicators and high-entropy prediction data, achieving a closed-form solution that predicts failures in advance and restarts to reduce total completion time, validated in a robot slot assembly experiment.
Optimal Energy Tank Initialization for Minimum Sensitivity to Model Uncertainties
Andrea Pupa, Cristian Secchi
OptimizationRobotic Intelligence
🎯 What it does: Propose a new energy tank initialization strategy that utilizes closed-loop state sensitivity to derive the precise boundaries of energy behavior, and employs nonlinear optimization to find the optimal trajectory and minimum initial energy under robot models with parameter uncertainty, achieving localization tasks.
Optimization-Based VINS: Consistency, Marginalization, and FEJ
Chuchu Chen, Guoquan Huang
OptimizationSimultaneous Localization and Mapping
🎯 What it does: This paper provides a comprehensive analysis of the FEJ method's application in nonlinear optimization-based visual inertial navigation systems (VINS), and offers guidance on correctly implementing FEJ in four common marginalization architectures.
Optimizing Algorithms from Pairwise User Preferences
L. Keselman, Aaron Steinfeld
OptimizationRobotic Intelligence
🎯 What it does: Propose the SortCMA algorithm, which uses pairwise user preferences for high-dimensional algorithm parameter optimization, and applies it to tuning commercial depth sensors and robot social navigation without real annotations;
Optimizing the Extended Fourier Mellin Transformation Algorithm
W. Jiang, Sören Schwertfeger
Pose EstimationSimultaneous Localization and MappingImage
🎯 What it does: An optimized algorithm (o-eFMT) based on the extended Fourier-Mellin transform (eFMT) was developed for visual odometry, improving uncertainty extraction, the objective function, and graph optimization in the small loop (three consecutive frames).
Orbital Head-Mounted Display: A Novel Interface for Viewpoint Control during Robot Teleoperation in Cluttered Environments
Sjoerd Kuitert, Luka Peternel
Robotic Intelligence
🎯 What it does: Proposed an Orbital Head-Mounted Display (OHMD) interface, allowing operators to simultaneously control a 6-DoF camera platform and a robotic arm, while experiencing a remote environment through a VR headset.
Orientation Control with Variable Stiffness Dynamical Systems
Youssef Michel, Dongheui Lee
Robotic Intelligence
🎯 What it does: Propose a closed-loop control algorithm for robots in rotational motion and damping adaptation.
Output Feedback Formation Control of a School of Robotic Fish with Artificial Lateral Line Sensing
A. Wolek, D. Paley
Robotic Intelligence
🎯 What it does: Proposed an estimation and control framework based on artificial lateral line perception to achieve stable parallel motion of robotic fish schools;
Overtaking Moving Obstacles with Digit: Path Following for Bipedal Robots via Model Predictive Contouring Control
K. S. Narkhede, I. Poulakakis
OptimizationRobotic Intelligence
🎯 What it does: Proposes a Model Predictive Contour Control (MPCC) method for selecting gaits along a global path to maximize path traversal while balancing precise tracking and rapid passage.
P2O-Calib: Camera-LiDAR Calibration Using Point-Pair Spatial Occlusion Relationship
Su Wang, Xuchong Qiu
Autonomous DrivingPoint Cloud
🎯 What it does: Proposes a novel un-calibrated target camera-LiDAR extrinsic calibration method based on 3D spatial occlusion relationships for 2D-3D edge point extraction and occlusion-guided point matching.
P4P: Conflict-Aware Motion Prediction for Planning in Autonomous Driving
Qiao Sun, Hang Zhao
Autonomous DrivingSafty and PrivacyPoint Cloud
🎯 What it does: Proposed a conflict-aware motion prediction method called P4P to enhance the safety of autonomous driving planning
PACT: Perception-Action Causal Transformer for Autoregressive Robotics Pre-Training
Rogerio Bonatti, Ashish Kapoor
Representation LearningRobotic IntelligenceTransformerSupervised Fine-TuningImagePoint Cloud
🎯 What it does: Proposes PACT (Perception-Action Causal Transformer), a self-supervised generative model based on Transformer architecture, for learning perception-action causal representations from robot data, and achieves performance improvements in multi-task scenarios (e.g., safe navigation, localization, and mapping) through fine-tuning of small task-specific networks.
PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting
Gabriele Tiboni, T. Tommasi
Robotic IntelligencePoint CloudBenchmark
🎯 What it does: Propose a framework capable of processing arbitrary 3D point clouds and predicting multiple unstructured trajectories for industrial painting tasks, and release the first public PaintNet dataset.
PanelPose: A 6D Pose Estimation of Highly-Variable Panel Object for Robotic Robust Cockpit Panel Inspection
Han Sun, Qixin Cao
Data SynthesisPose EstimationRobotic IntelligenceSimultaneous Localization and MappingImage
🎯 What it does: Proposed a 6D pose estimation method called PanelPose for handling highly variable panel objects in robot inspection scenarios within aviation cockpits.
PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation
Jianbiao Mei, Yong Liu
SegmentationAutonomous DrivingPoint Cloud
🎯 What it does: Propose the PANet framework, utilizing sparse instance proposal (SIP) and an instance aggregation module for LiDAR panoramic segmentation, removing the offset branch and enhancing performance on large objects.
Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots
Yue Pan, C. Stachniss
Pose EstimationSimultaneous Localization and MappingAgriculture Related
🎯 What it does: Proposed an online multi-resolution panoramic mapping system for simultaneously performing fruit 3D shape reconstruction and pose estimation in multi-resolution maps.
PanopticNDT: Efficient and Robust Panoptic Mapping
Daniel Seichter, H. Groß
Computational EfficiencyRobotic IntelligenceSimultaneous Localization and MappingPoint Cloud
🎯 What it does: Proposed an efficient and robust panoramic mapping method called PanopticNDT based on the Occupancy Normal Distribution Transform (NDT).
Parallel Cell Array Patterning and Target Cell Lysis on an Optoelectronic Micro-Well Device
Chunyuan Gan, Lin Feng
Biomedical Data
🎯 What it does: Developed an electrical method based on a photoelectrode microhole array, achieving cell arraying and electroporation lysis of target cells through light-induced depolarization forces.
Parallel-Jaw Gripper and Grasp Co-Optimization for Sets of Planar Objects
Rebecca H. Jiang, Alberto Rodriguez
OptimizationRobotic Intelligence
🎯 What it does: Proposed a joint optimization framework for planar parallel clamp-type grippers and grasping actions targeting multi-faceted objects
Part-level Scene Reconstruction Affords Robot Interaction
Zeyu Zhang, Hangxin Liu
OptimizationRobotic Intelligence
🎯 What it does: Proposes a component-level interactive scene reconstruction method, which reassembles objects using original shapes to accurately replicate observed physical scenes and support robot interaction with rigid and articulated objects.
Path-Following Control with Path and Orientation Snap-In
C. Hartl-Nesic, Andreas Kugi
Robotic Intelligence
🎯 What it does: Proposed an extension of multi-path path following control, and designed two physical human-robot interaction modes: path snap-in and orientation snap-in, using attractive forces to guide the robot's end-effector to the path or predefined posture.
Perceptive Hexapod Legged Locomotion for Climbing Joist Environments
Zixian Zang, A. Zakhor
Depth EstimationKnowledge DistillationRobotic IntelligenceReinforcement LearningImage
🎯 What it does: Developed a perceptual gait model utilizing self-perspective depth maps, enabling a six-legged robot to navigate narrow attic wooden truss structures and achieve zero-shot transfer from simulation to reality.
Performance Comparison of Teleoperation Interfaces for Ultra-Lightweight Anthropomorphic Arms
Filip Zorić, A. Ollero
Pose EstimationRobotic Intelligence
🎯 What it does: This paper conducted a comparative evaluation of the performance of three teleoperation interfaces on an ultra-lightweight (<3 kg) dual-arm humanoid robot, aiming to achieve flexibility, accuracy, and agility in complex manipulation tasks;
Perirobot Space Representation for HRI: Measuring and Designing Collaborative Workspace Coverage by Diverse Sensors
Jakub Rozlivek, M. Hoffmann
Robotic IntelligenceSimultaneous Localization and MappingMultimodality
🎯 What it does: Defined the space around the robot that needs to be monitored, represented all sensor data as occupancy information, and used occupancy-based metrics to calculate the effectiveness of sensor coverage in the workspace.
Perseus AUV: Towards Linear Convoying of Agile A-Sized AUVs Through Acoustic Track-and-Trail
N. Rypkema, Michael Triantafyllou
Autonomous Driving
🎯 What it does: Designed and demonstrated a Perseus A-sized AUV equipped with a low-cost passive inverted ultra-short baseline (piUSBL) acoustic receiver system, capable of performing acoustic tracking and following a leading vehicle carrying an acoustic source, and achieving good directional stability and maneuverability through tuna-inspired variable fins; the system was validated through field experiments in Massachusetts' Charles River.
Persuasive Polite Robots in Free-Standing Conversational Groups
S. Zojaji, C. Peters
Robotic Intelligence
🎯 What it does: This paper experimentally investigates the use of six politeness behaviors based on Brown and Levinson's politeness theory by a humanoid Pepper robot in a free-standing conversation group, evaluating human participants' decisions to join positions (especially more distant ones) and their perception of the robot's politeness.
Physical Contact with Wall using a Multirotor UAV Equipped with Add-On Thruster for Inspection Work
Takamasa Kominami, K. Shimonomura
Robotic IntelligenceUltrasoundPhysics Related
🎯 What it does: Developed a multirotor drone equipped with a single horizontal thruster for physical contact non-destructive testing on vertical walls.
Physics-Informed Learning to Enable Robotic Screw-Driving Under Hole Pose Uncertainties
O. Manyar, Satyandra K. Gupta
Robotic IntelligencePhysics Related
🎯 What it does: Design a mobile manipulator system using active impedance control and passive elastic driving tools, combining physics-informed learning to automatically model screw tip motion, detect failures, and perform corrections;
PIMbot: Policy and Incentive Manipulation for Multi-Robot Reinforcement Learning in Social Dilemmas
Shahab Nikkhoo, Cong Liu
Reinforcement Learning
🎯 What it does: Propose PIMbot that adjusts the reward function in multi-robot collaboration through strategy and incentive manipulation methods to influence cooperative behavior in social dilemmas.
Placing by Touching: An Empirical Study on the Importance of Tactile Sensing for Precise Object Placing
Luca Lach, G. Chalvatzaki
Robotic IntelligenceMultimodality
🎯 What it does: Proposes an object placement method that combines tactile feedback with proprioception, using a neural network called PlaceNet to estimate rotation matrices and correct the gripper for stable placement.
Planning and Control for a Dynamic Morphing-Wing UAV Using a Vortex Particle Model
Gino Perrotta, Joseph L. Moore
OptimizationRobotic IntelligencePhysics Related
🎯 What it does: Proposed a vortex particle model and a model-based controller to account for unsteady aerodynamics during post-stall maneuvering in dynamic variable-wing UAVs.
PLPL-VIO: A Novel Probabilistic Line Measurement Model for Point-Line-Based Visual-Inertial Odometry
Zewen Xu, Xin Jin
Pose EstimationOptimizationSimultaneous Localization and Mapping
🎯 What it does: Proposes a visual-inertial odometry (VIO) system based on point-line features, and introduces a new line feature measurement model and uncertainty handling method.
Point2Point: A Framework for Efficient Deep Learning on Hilbert Sorted Point Clouds with Applications in Spatio-Temporal Occupancy Prediction
A. Pandhare
Autonomous DrivingComputational EfficiencyPoint Cloud
🎯 What it does: Proposes using Hilbert space-filling curves to construct a one-dimensional ordering that preserves locality for representing point clouds, and designs a neural network architecture called Point2Point that can effectively learn from Hilbert-ordered point clouds.
Poly-MOT: A Polyhedral Framework For 3D Multi-Object Tracking
Xiaoyu Li, K. Wang
Object TrackingAutonomous DrivingPoint Cloud
🎯 What it does: Proposes the Poly-MOT framework, which selects the most suitable tracking criteria for different object categories, employs multiple motion models and three custom similarity metrics, and adopts a two-stage data association strategy.
Polymer-Based Self-Calibrated Optical Fiber Tactile Sensor
Wen-Tzu Chen, Jia Pan
Robotic Intelligence
🎯 What it does: Designed and implemented a polymer-based self-calibrating optical fiber tactile sensor capable of separating normal force and shear force and calibrating according to the size of the contacted object.
Polynomial-Based Online Planning for Autonomous Drone Racing in Dynamic Environments
Qianhao Wang, Fei Gao
OptimizationRobotic Intelligence
🎯 What it does: Propose an online replanning framework for UAV racing in dynamic environments, using polynomial trajectory representation to optimize the balance between high speed and obstacle avoidance, and incorporating hard constraints to ensure passing through intermediate track points; for dynamic obstacles, parallel multi-topology planning is adopted to avoid local optima causing time loss, ultimately achieving first place in the DJI Robomaster UAV competition, completing the track in half the time of the second place.
POMDP-Guided Active Force-Based Search for Robotic Insertion
Chen Wang, Wei Zhang
Robotic IntelligenceReinforcement Learning
🎯 What it does: Proposed a proactive dynamics search strategy based on POMDP, utilizing contact information for insertion tasks
PoseFusion: Robust Object-in-Hand Pose Estimation with SelectLSTM
Yuyang Tu, Jianwei Zhang
Pose EstimationRecurrent Neural NetworkImageMultimodalityBenchmark
🎯 What it does: Proposes PoseFusion, a multimodal fusion method combining vision and tactile sensing for robust hand-held object pose estimation, and creates a 6D hand-held object pose dataset containing RGB-D, tactile, and proprioceptive data.
Powered Knee and Ankle Prosthesis Control for Adaptive Ambulation at Variable Speeds, Inclines, and Uneven Terrains
Liam M. Sullivan, Tommaso Lenzi
Robotic Intelligence
🎯 What it does: Designed and implemented a knee and ankle electric prosthetic controller capable of continuously adapting to varying walking speeds, slopes, and uneven terrains.
Pre-and Post-Contact Policy Decomposition for Non-Prehensile Manipulation with Zero-Shot Sim-To-Real Transfer
Minchan Kim, Beomjoon Kim
Domain AdaptationRobotic IntelligenceReinforcement Learning
🎯 What it does: Proposed a non-grasping operating system that can move objects to target positions through multiple contact mode transitions and by leveraging environmental contact points, achieving zero-shot transfer from simulation to real-world environments
Pred-NBV: Prediction-Guided Next-Best-View Planning for 3D Object Reconstruction
Harnaik Dhami, Pratap Tokekar
RestorationGenerationTransformerPoint CloudMesh
🎯 What it does: Proposed the Pred-NBV method for 3D object reconstruction, using prediction to guide the planning of the next best view.
Predicting Center of Mass by Iterative Pushing for Object Transportation and Manipulation
Steven M. Hyland, C. Onal
Robotic Intelligence
🎯 What it does: Propose a method using small mobile robots to estimate the center of mass of approximately planar objects via iterative pushing
Predicting Energy Consumption and Traversal Time of Ground Robots for Outdoor Navigation on Multiple Types of Terrain
Matthias Eder, Gerald Steinbauer-Wagner
OptimizationRobotic IntelligenceConvolutional Neural Network
🎯 What it does: A data-driven supervised machine learning approach is used to construct a cost representation model for energy consumption and travel time across different outdoor terrains;
Primitive Skill-Based Robot Learning from Human Evaluative Feedback
Ayano Hiranaka, Ruohan Zhang
Robotic IntelligenceReinforcement Learning from Human FeedbackReinforcement Learning
🎯 What it does: Proposes a framework called SEED, which combines reinforcement learning with human evaluation feedback (RLHF) and skill-based reinforcement learning to address sparse rewards and safety issues in long-horizon robotic manipulation tasks.
Principled ICP Covariance Modelling in Perceptually Degraded Environments for the EELS Mission Concept
W. Talbot, V. Ila
Simultaneous Localization and MappingPoint Cloud
🎯 What it does: Proposed a principled modeling approach for LiDAR point-to-plane ICP covariance, and conducted comparative evaluation of new and old models within the complete SLAM pipeline.
Printable Bistable Structures for Programmable Frictional Skins of Soft-Bodied Robots
Tung D. Ta, Yoshihiro Kawahara
Robotic Intelligence
🎯 What it does: A programmable friction skin based on bistable bellow structures is proposed, which can switch between two folded states, dynamically alter the contact points with the ground, and achieve tunable anisotropic friction properties.
Prioritized Planning for Target-Oriented Manipulation via Hierarchical Stacking Relationship Prediction
Zewen Wu, Nanning Zheng
Robotic IntelligenceImage
🎯 What it does: Propose a goal-oriented priority planning method based on hierarchical stacking relationship prediction
Privacy-Preserving and Uncertainty-Aware Federated Trajectory Prediction for Connected Autonomous Vehicles
Muzi Peng, Lili Su
Autonomous DrivingFederated LearningSafty and PrivacySequential
🎯 What it does: Proposed FLTP (Federated Learning Trajectory Prediction) and ALFLTP (FLTP with Active Learning) algorithms, achieving privacy-preserving and uncertainty-aware trajectory prediction on connected autonomous vehicles.
Proactive Model Predictive Control with Multi-Modal Human Motion Prediction in Cluttered Dynamic Environments
Lukas Heuer, K. Arras
Autonomous DrivingOptimizationRobotic IntelligenceMultimodality
🎯 What it does: Proposes a new framework that integrates multimodal human motion prediction into nonlinear model predictive control for robot navigation in crowded dynamic environments.
Proactive Opinion-Driven Robot Navigation Around Human Movers
Charlotte Cathcart, N. Leonard
Robotic Intelligence
🎯 What it does: Proposed, analyzed, and experimentally verified an active robot social navigation method driven by the robot's 'views' formed from the direction and degree of movement of human pedestrians, which evolve over time according to nonlinear dynamics and utilize attention to social cues to determine passage strategies.
Probabilistic Guarantees for Nonlinear Safety-Critical Optimal Control
Prithvi Akella, A. Ames
OptimizationSafty and Privacy
🎯 What it does: Provides three algorithms to generate three categories of probabilistic guarantees for general nonlinear safety-critical finite-time optimal controllers: optimality of the solution, recursive feasibility, and maximum controller runtime.
Probabilistic Slide-support Manipulation Planning in Clutter
Shusei Nagato, K. Harada
Robotic Intelligence
🎯 What it does: Proposed a dual-arm robot sliding-support strategy, where one arm slides the target object out of a cluttered environment while the other arm supports surrounding objects to prevent clutter collapse, achieving safe and efficient extraction of the target object.
Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments
Xiaoyi Cai, J. How
Autonomous Driving
🎯 What it does: Proposed a probabilistic off-road traversability model based on the empirical distribution of trajectory parameters, performing risk-aware path planning under the no-slip assumption.
Programable On-Chip Fabrication of Magnetic Soft Micro-Robot
Yuke Li, T. Arai
Robotic Intelligence
🎯 What it does: Proposed a programmable preparation method for magnetic soft microrobots through an on-chip photopolymerization system, assembling superparamagnetic nanoparticles via their magnetic anisotropy and fixing them using hydrogel photopolymerization, successfully fabricating joint rotation mechanisms and snake-like microrobots that achieve the intended motion.
Projecting Robot Intentions Through Visual Cues: Static vs. Dynamic Signaling
Shubham D. Sonawani, H. B. Amor
Robotic IntelligenceImageVideo
🎯 What it does: Compare the effectiveness of static and dynamic visual signals in collaborative object sorting tasks, assess their impact on human behavior, and quantify the information transfer between visual signals and human behavior using information theory methods.
Proprioception and Reaction for Walking Among Entanglements
Justin K. Yim, Aaron M. Johnson
Robotic Intelligence
🎯 What it does: Proprioceptive method and reactive strategy for legged robots to perceive entanglement in entangled environments and perform untangling during the swing phase
Proprioception and Tail Control Enable Extreme Terrain Traversal by Quadruped Robots
Yanhao Yang, Aaron M. Johnson
Robotic Intelligence
🎯 What it does: Developed proprioception-based gait planning and two-degree-of-freedom tail control, enabling quadruped robots to dynamically traverse extreme terrains with sudden height variations.
Proprioceptive External Torque Learning for Floating Base Robot and its Applications to Humanoid Locomotion
Daegyu Lim, Jaeheung Park
Robotic IntelligenceRecurrent Neural NetworkTime SeriesSequential
🎯 What it does: Learn and estimate external joint torques and contact torques of a floating base robot using only proprioceptive sensors such as encoders and IMUs, and verify their application in gait control.
Protective Skin Mechanism with an Exhaustive Arrangement of Tiny Rigid Bodies for Soft Robots: Evaluation of Puncture Resistance, Elasticity, and Descaling Resistance of the Scale Mechanism
K. Tadakuma, S. Tadokoro
Robotic Intelligence
🎯 What it does: Proposed a skin protection mechanism for soft robots using densely packed small rigid bodies, and tested by sewing small pieces with Kevlar threads on elastic sheets.
Prototypical Contrastive Transfer Learning for Multimodal Language Understanding
Seitaro Otsuki, Komei Sugiura
Domain AdaptationRepresentation LearningData-Centric LearningContrastive LearningMultimodality
🎯 What it does: Propose a novel multimodal language understanding transfer learning method called Prototypical Contrastive Transfer Learning (PCTL), and apply it to locate target objects in a home environment based on free-form natural language instructions.
Provably Correct Sensor-Driven Path-Following for Unicycles Using Monotonic Score Functions
Be Clark, Hasan A. Poonawala
Robotic IntelligenceScore-based Model
🎯 What it does: Designed a stable controller based on sensor measurements for path tracking in robots with single-wheel kinematics (e.g., wheeled mobile robots);
ProxMaP: Proximal Occupancy Map Prediction for Efficient Indoor Robot Navigation
V. Sharma, Pratap Tokekar
Computational EfficiencyRobotic IntelligenceSimultaneous Localization and Mapping
🎯 What it does: Designed a self-supervised occupancy prediction technique ProxMaP to predict the occupancy status of areas near the robot to accelerate navigation
Pseudo Inputs Optimisation for Efficient Gaussian Process Distance Fields
Lan Wu, Teresa Vidal-Calleja
OptimizationComputational EfficiencySimultaneous Localization and Mapping
🎯 What it does: Proposes an efficient distance field representation based on Gaussian processes, utilizing pseudo-input optimization to generate accurate distance fields and iteratively building maps in dynamic environments.
Pseudo-Stereo++: Cycled Generative Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving
Ahmed Elhagry, Abdulmotaleb El-Saddik
Object DetectionAutonomous DrivingConvolutional Neural NetworkContrastive LearningImage
🎯 What it does: Proposed a Cyclic Generative Pseudo Stereo (CGPS) architecture that generates right views from monocular left views, creating pseudo stereo pairs for stereo 3D detectors to use.
PTDRL: Parameter Tuning Using Deep Reinforcement Learning
Elias Goldsztejn, R. Brafman
Hyperparameter SearchReinforcement Learning
🎯 What it does: Propose the PTDRL parameter tuning strategy, which automatically selects parameters from a fixed parameter set to maximize expected rewards, thereby improving indoor social navigation performance.
Push to Know! - Visuo-Tactile Based Active Object Parameter Inference with Dual Differentiable Filtering
Anirvan Dutta, Mohsen Kaboli
Robotic IntelligenceMultimodality
🎯 What it does: Estimate key physical parameters of objects (shape, friction coefficient, mass, center of gravity, inertia) through non-grasping pushing operations combined with visual and tactile perception.
PuSHR: A Multirobot System for Nonprehensile Rearrangement
Sidharth Talia, S. Srinivasa
OptimizationRobotic Intelligence
🎯 What it does: This study addresses the problem of non-grasping object rearrangement using vehicle-type pushing robots, constructing the PuSHR multi-robot system. It optimizes task allocation and trajectory planning in the offline phase, and achieves decentralized trajectory tracking in the online phase.
Pyramid Semantic Graph-Based Global Point Cloud Registration with Low Overlap
Zhijian Qiao, S. Shen
Pose EstimationAutonomous DrivingOptimizationGraph Neural NetworkPoint Cloud
🎯 What it does: Propose a global point cloud registration framework based on semantic graphs to address registration problems under low overlap conditions.
QDP: Learning to Sequentially Optimise Quasi-Static and Dynamic Manipulation Primitives for Robotic Cloth Manipulation
David Blanco Mulero, Ville Kyrki
OptimizationRobotic IntelligenceReinforcement Learning
🎯 What it does: Propose the QDP method to optimize the speed, trajectory, and pickup/drop-off positions of fabric manipulation primitives, achieving adaptability to different fabric properties.
Quadratic Dynamic Matrix Control for Fast Cloth Manipulation
Edoardo Caldarelli, Carme Torras
OptimizationRobotic Intelligence
🎯 What it does: Integrate Quadratic Dynamic Matrix Control (QDMC) with chance constraints for robotic fabric manipulation.
Quadrupedal Footstep Planning Using Learned Motion Models of a Black-Box Controller
I. Taouil, Sven Behnke
OptimizationRobotic IntelligenceImage
🎯 What it does: This paper learns a motion model that maps high-level velocity commands from a black-box locomotion controller to the center of mass (CoM) and toe movements. These models are combined with a variant of the A* algorithm to plan CoM trajectories, footstep sequences, and corresponding high-level velocity commands based on visual information, enabling safe quadruped robot locomotion over irregular terrain.
Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained Environments
Calvin Tanama, Alina Roitberg
RecognitionAutonomous DrivingOptimizationComputational EfficiencyKnowledge DistillationConvolutional Neural Network
🎯 What it does: Propose a lightweight framework that improves 3D MobileNet by combining knowledge distillation and model quantization to achieve driver behavior recognition in resource-constrained environments.
RACECAR - The Dataset for High-Speed Autonomous Racing
A. Kulkarni, Madhur Behl
Autonomous DrivingMultimodalityPoint Cloud
🎯 What it does: Created and released the RACECAR high-speed full-scale autonomous racing car dataset, collected multi-modal sensor data, covering 11 track scenarios, 27 events, and 6.5 hours of racing;
RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Conditions
Jialu Wang, Andrew Markham
Autonomous DrivingAdversarial AttackImage
🎯 What it does: Propose a robust adversarial data augmentation method called RADA to enhance the robustness of camera localization under complex conditions.
RAIST: Learning Risk Aware Traffic Interactions via Spatio-Temporal Graph Convolutional Networks
Videsh Suman, Aniket Bera
Autonomous DrivingGraph Neural Network
🎯 What it does: Propose a self-perspective driving framework based on spatiotemporal traffic graphs, and train graph edges using spatiotemporal graph convolutional networks (ST-GCN) to learn risk-aware traffic interaction representations
RAMP: Hierarchical Reactive Motion Planning for Manipulation Tasks Using Implicit Signed Distance Functions
V. Vasilopoulos, Volkan Isler
OptimizationRobotic Intelligence
🎯 What it does: Proposes RAMP—a hierarchical motion planning framework that combines sampling-based and reactive methods for manipulation tasks; generates trajectories using a novel MPPI controller, which are then asynchronously followed by a local vector field controller; achieves rapid path planning, task constraint satisfaction, and obstacle-safe responses in desktop cleaning scenarios.
Range-based GP Maps: Local Surface Mapping for Mobile Robots using Gaussian Process Regression in Range Space
Margaret Hansen, David Wettergreen
Robotic IntelligenceSimultaneous Localization and MappingPoint Cloud
🎯 What it does: Proposed and implemented a Gaussian Process (GP) map based on LiDAR range, representing terrain by directly modeling the sensor range in spherical space.
Rapid Grasping of Fabric Using Bionic Soft Grippers with Elastic Instability
Zechen Xiong, Hod Lipson
Robotic Intelligence
🎯 What it does: Designed and demonstrated an elastic unstable soft gripper inspired by the pinching action of the human thumb and index finger, capable of achieving wide-range rapid closure;
RaPlace: Place Recognition for Imaging Radar using Radon Transform and Mutable Threshold
Hyesu Jang, Ayoung Kim
RecognitionRetrievalImage
🎯 What it does: Propose a radar-based place recognition method that calculates similarity using sinogram images from the Radon transform and frequency domain cross-correlation.
RaSpectLoc: RAman SPECTroscopy-dependent robot LOCalisation
C. Thirgood, Simon J. Hadfield
Robotic IntelligenceSimultaneous Localization and MappingBenchmark
🎯 What it does: Proposed and implemented the first robot localization algorithm based on material composition, which utilizes a Raman spectrometer to acquire material information and integrates it with visual, structural, and semantic features to improve localization accuracy.
Re-Evaluating Parallel Finger-Tip Tactile Sensing for Inferring Object Adjectives: An Empirical Study
Fangyi Zhang, Peter Corke
ClassificationRobotic IntelligenceTabular
🎯 What it does: Investigate the contribution of individual taxels in fingertip tactile sensors when inferring object adjectives.
Re-Thinking Classification Confidence with Model Quality Quantification
Yancheng Pan, Huijing Zhao
ClassificationExplainability and Interpretability
🎯 What it does: Proposed a confidence estimation method based on model quality quantization, and developed two evaluation metrics, MQ-Repres and MQ-Discri, as well as the MQ-Conf confidence estimation;
Reachability-Aware Collision Avoidance for Tractor-Trailer System with Non-Linear MPC and Control Barrier Function
Yucheng Tang, Björn Hein
Autonomous DrivingOptimization
🎯 What it does: Proposes a method combining reachability-aware model predictive control (MPC) with discrete control barrier functions (CBF) for backward obstacle avoidance in tractor-trailer systems.
Reactive and Safe Co-Navigation with Haptic Guidance
Mela C. Coffey, Alyssa Pierson
Autonomous DrivingSafty and Privacy
🎯 What it does: Propose a human-robot collaborative navigation algorithm where the robot uses tactile feedback to achieve collision avoidance and path suggestions, supporting humans in high-level path decision-making.
Read the Room: Adapting a Robot's Voice to Ambient and Social Contexts
Emma Hughson, Angelica Lim
Robotic IntelligenceAudio
🎯 What it does: Researched and implemented the process of robots adjusting their voice style according to social and environmental contexts in different environments (such as quiet, dim, bright, or noisy), primarily validated in dining scenarios.
Real is Better than Perfect: Sim-to-Real Robotic System in Secondary School Education
Jiasi Gao, Guyue Zhou
Robotic Intelligence
🎯 What it does: Built a simulation-to-real robot system where students can optimize algorithms in a simulation environment and verify them in a remote physical laboratory, equipped with an automatic submit-test-reset subsystem to enable 24/7 testing support.
Real-time Dynamic Bipedal Avoidance
Tianze Wang, Christian Hubicki
OptimizationRobotic Intelligence
🎯 What it does: Proposes a real-time motion planning and multi-body control framework to enable dynamic biped robots to avoid collisions with multiple moving obstacles and automatically switch between stance and gait collision avoidance modes.
Real-Time Elevation Mapping with Bayesian Ground Filling and Traversability Analysis for UGV Navigation
Han Xie, Qiang Liu
Autonomous DrivingSimultaneous Localization and MappingPoint Cloud
🎯 What it does: Using sparse point cloud data from a single LiDAR, real-time generation of dense terrain height maps and traversability analysis is achieved, capable of distinguishing vertical obstacles, hanging objects, etc.
Real-Time Failure-Adaptive Control for Dynamic Robots
Jacob Hackett, Christian Hubicki
OptimizationRobotic Intelligence
🎯 What it does: Propose a real-time failure-adaptive control framework that can instantly learn failure probabilities and replan behaviors when dynamic robots (e.g., drones) encounter failures.
Real-Time Model-Free Deep Reinforcement Learning for Force Control of a Series Elastic Actuator
Ruturaj S. Sambhus, A. Leonessa
Robotic IntelligenceReinforcement Learning
🎯 What it does: On the Hardware Series Elastic Actuator (SEA) swing system, a Proximal Policy Optimization (PPO) algorithm is used to train a deep reinforcement learning (DRL) policy to achieve force tracking control with a frequency range of 0.05 Hz to 0.35 Hz and an amplitude of 50 N. The system is trained continuously for over 21 hours without human intervention, with safety mechanisms implemented during training to ensure hardware safety.
Real-Time Motion Planning Framework for Autonomous Vehicles with Learned Committed Trajectory Distribution
Minsoo Kim, Jaeheung Park
Autonomous Driving
🎯 What it does: Proposed a real-time motion planning framework that uses a neural network to predict a committed trajectory distribution as a sampling bias to achieve near-optimal continuous path planning;
Real-Time NMPC for an Automated Valet Parking with Load-Based Safety Constraints and a Path-Parametric Model
B. B. Carlos, Benoît Pelourdeau
Autonomous DrivingOptimization
🎯 What it does: Propose a real-time nonlinear model predictive control (NMPC) framework for an automatic valet parking system, aiming to reduce retrieval time and ensure vehicle and robot safety through acceleration constraints.
Real-Time Pose Estimation of Rats Based on Stereo Vision Embedded in a Robotic Rat
Xiaowen Guo, Qing Shi
Pose EstimationRobotic IntelligenceImage
🎯 What it does: A real-time rat pose estimation system based on stereo vision, designed with a lightweight high-resolution network RRKDNet for keypoint detection, and achieved pose reconstruction through stereo vision models and robot coordinate transformation, completing real-time simulation experiments.
Real-Time RRT* with Signal Temporal Logic Preferences
Alexis Linard, Jana Tumova
OptimizationRobotic Intelligence
🎯 What it does: In dynamic obstacle environments, Signal Temporal Logic (STL) constraints and preferences are integrated into the real-time rapidly exploring random tree (RT-RRT*) motion planning algorithm, and a cost function is proposed to guide the algorithm toward the most robust (i.e., STL-compliant) solution.
Real-Time Simultaneous Multi-Object 3D Shape Reconstruction, 6DoF Pose Estimation and Dense Grasp Prediction
Shubh Agrawal, Volkan Isler
Pose EstimationComputational EfficiencyRobotic IntelligenceImagePoint Cloud
🎯 What it does: Propose a real-time method to achieve multi-object 3D shape reconstruction, 6DoF pose estimation, and dense grasp point prediction from a single RGB-D image.
Real-Time Trajectory-Based Social Group Detection
Simindokht Jahangard, Hamid Rezatofighi
Object TrackingComputational EfficiencyRecurrent Neural NetworkGraph Neural NetworkGraphTime SeriesSequential
🎯 What it does: A real-time social group detection framework based on motion trajectories is proposed, treating individuals as nodes in a graph, using LSTM to encode trajectories, constructing edges based on inter-trajectory distances, and achieving group identification through an improved graph transformer and graph clustering loss.