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Morphological and Embedded Computation in a Self-contained Soft Robotic HandPreprint

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Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks

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sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control

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Constrained Stein Variational Trajectory Optimization

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A dynamic keypoint selection network for 6DoF pose estimation

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Skill-CPD: Real-time Skill Refinement for Shared Autonomy in Manipulator Teleoperation

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Task-grasping from a demonstrated human strategy

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Household Clothing Set and Benchmarks for Characterising End-Effector Cloth Manipulation

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Adaptive Tactile Force Control in a Parallel Gripper With Low Positioning Resolution

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Deep instance segmentation and 6D object pose estimation in cluttered scenes for robotic autonomous grasping

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Ontology-Assisted Generalisation of Robot Action Execution Knowledge

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Orientation Attentive Robotic Grasp Synthesis with Augmented Grasp Map RepresentationPreprint

Chalvatzaki, G., Gkanatsios, N., Maragos, P. A., & Peters, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.05123 · Cited by 16

DexGANGrasp: Dexterous Generative Adversarial Grasping Synthesis for Task-Oriented Manipulation

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3D Reconstruction in Robotics: A Comprehensive Review

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Tactile SoftHand-A: 3D-printed, tactile, highly underactuated, anthropomorphic robot hand with an antagonistic tendon mechanism

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Graph-based Cluttered Scene Generation and Interactive Exploration using Deep Reinforcement Learning

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Visual-Tactile Fusion for 3D Objects Reconstruction from a Single Depth View and a Single Gripper Touch for Robotics Tasks

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Primitive shape recognition from real-life scenes using the PointNet deep neural network

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Object classification system using temperature variation of smart finger device via machine learning

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Contact Transfer: A Direct, User-Driven Method for Human to Robot Transfer of Grasps and Manipulations

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VIPose: Real-time Visual-Inertial 6D Object Pose Tracking

Ge, R., & Loianno, G. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636283 · Cited by 12

Online active and dynamic object shape exploration with a multi-fingered robotic hand

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Amodal 3D Reconstruction for Robotic Manipulation via Stability and ConnectivityPreprint

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Recursive Metropolis-Hastings naming game: symbol emergence in a multi-agent system based on probabilistic generative models

Inukai, J., Taniguchi, T., Taniguchi, A., & Hagiwara, Y. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1229127 · Cited by 12

SORDI.ai: large-scale synthetic object recognition dataset generation for industries

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Grasp Anything: Combining Teacher-Augmented Policy Gradient Learning with Instance Segmentation to Grasp Arbitrary Objects

Mosbach, M., & Behnke, S. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610700 · Cited by 10

Empirical Comparison of Four Stereoscopic Depth Sensing Cameras for Robotics Applications

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A Bio-inspired Grasp Stiffness Control for Robotic Hands

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Improving Robot Success Detection using Static Object Data

Scalise, R., Thomason, J., Bisk, Y., & Srinivasa, S. S. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968142 · Cited by 10

Binary-LoRAX: Low-Latency Runtime Adaptable XNOR Classifier for Semi-Autonomous Grasping with Prosthetic Hands

Fasfous, N., Vemparala, M. R., Frickenstein, A., Badawy, M., Hundhausen, F., Hoefer, J., Nagaraja, N.-S., Unger, C., Vogel, H.-J., Becker, J., et al. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561045 · Cited by 10

Tracking 6-DoF Object Motion from Events and Frames

Li, H., & Stueckler, J. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561760 · Cited by 11

RFFCE: Residual Feature Fusion and Confidence Evaluation Network for 6DoF Pose Estimation

Meng, Q., Ji, S., Zhu, S., Jin, T., Li, T., Gu, J. J., & Song, W. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160448 · Cited by 10

Scalable and Time-Efficient Bin-Picking for Unknown Objects in Dense Clutter

Raj, P., Behera, L., & Sandhan, T. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3324610 · Cited by 10

Sliding Touch-Based Exploration for Modeling Unknown Object Shape with Multi-Fingered Hands

Chen, Y.-T., Tekden, A. E., Deisenroth, M. P., & Bekiroglu, Y. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342303 · Cited by 10

Configuration Lattices for Planar Contact Manipulation Under Uncertainty

Koval, M. C., Hsu, D., Pollard, N. S., & Srinivasa, S. S. (2020) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-43089-4_49 · Cited by 10

Robust Cylinder Estimation in Point Clouds from Pairwise Axes Similarities

Pistellato, M., Bergamasco, F., Albarelli, A., & Andrea Torsello, A. (2019) · International Conference on Pattern Recognition Applications and Methods · doi:10.5220/0007401706400647 · Cited by 10

Par3DNet: Using 3DCNNs for Object Recognition on Tridimensional Partial Views

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Efficiently finding poses for multiple grasp types with partial point clouds by uncoupling grasp shape and scale

Hegedus, M., Gupta, K. K., & Mehrandezh, M. (2022) · Autonomous Robots · doi:10.1007/s10514-022-10049-6 · Cited by 10

Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge

Yokoyama, N., Luo, Q., Batra, D., & Ha, S. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981743 · Cited by 13

Neural Scanning: Rendering and determining geometry of household objects using Neural Radiance Fields

Erich, F., Bourreau, B., Tan, C. K., Caron, G., Yoshiyasu, Y., & Ando, N. (2023) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii55687.2023.10039147 · Cited by 10

A competitive learning scheme for deep neural network pattern classifier training

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Experience-based optimization of robotic perception

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A Survey of Behavior Learning Applications in Robotics -- State of the Art and PerspectivesPreprint

Fabisch, A., Petzoldt, C., Otto, M., & Kirchner, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01868 · Cited by 18

IPA-3D1K: A Large Retail 3D Model Dataset for Robot Picking

Lindermayr, J., Odabaşı, Ç., Jordan, F., Graf, F., Knak, L., Kraus, W., Bormann, R., & Huber, M. F. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342260 · Cited by 9

Learn from Incomplete Tactile Data: Tactile Representation Learning with Masked Autoencoders

Cao, G., Jiang, J., Bollegala, D., & Luo, S. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341788 · Cited by 11

Rapid Motor Adaptation for Robotic Manipulator Arms

Liang, Y., Ellis, K., & Henriques, J. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01552 · Cited by 9

BaRiFlex: A Robotic Gripper with Versatility and Collision Robustness for Robot Learning

Jeong, G.-C., Bahety, A., Pedraza, G., Deshpande, A. D., & Martín-Martín, R. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802024 · Cited by 9

Two-Stage Grasping: A New Bin Picking Framework for Small Objects

Cao, H., Zhou, J., Huang, J., Li, Y., Meng, N. C., Cao, R., Dou, Q., & Liu, Y. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160608 · Cited by 9

Revisiting Fully Convolutional Geometric Features for Object 6D Pose Estimation

Corsetti, J., Boscaini, D., & Poiesi, F. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00224 · Cited by 9

EDOPT: Event-camera 6-DoF Dynamic Object Pose Tracking

Glover, A., Gava, L., Li, Z., & Bartolozzi, C. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611511 · Cited by 11

Where can i do this? Geometric Affordances from a Single Example with the Interaction Tensor

Ruiz, E., & Mayol-Cuevas, W. W. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8462835 · Cited by 9

Recurrent processing improves occluded object recognition and gives rise to perceptual hysteresis

Ernst, M., Burwick, T., & Triesch, J. (2021) · Journal of Vision · doi:10.1167/jov.21.13.6 · Cited by 12

SporeAgent: Reinforced Scene-level Plausibility for Object Pose Refinement

Bauer, D., Patten, T., & Vincze, M. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00027 · Cited by 9

Embodied object representation learning and recognition

Van de Maele, T., Verbelen, T., Çatal, O., & Dhoedt, B. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.840658 · Cited by 9

Simultaneous Object Reconstruction and Grasp Prediction using a Camera-centric Object Shell Representation

Chavan-Dafle, N., Popovych, S., Agrawal, S., Lee, D. D., & Isler, V. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981955 · Cited by 9

Efficient collection and automatic annotation of real-world object images by taking advantage of post-diminished multiple visual markers

Kiyokawa, T., Tomochika, K., Takamatsu, J., & Ogasawara, T. (2019) · Advanced Robotics · doi:10.1080/01691864.2019.1697750 · Cited by 9

Shape Completion Enabled Robotic GraspingPreprint

Varley, J., DeChant, C., Richardson, A., Ruales, J., & Allen, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.08546 · Cited by 9

Multi-Fingered Grasp Planning via Inference in Deep Neural NetworksPreprint

Lu, Q., der Merwe, M. V., Sundaralingam, B., & Hermans, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.09242 · Cited by 9

SymmetryNet: Learning to Predict Reflectional and Rotational Symmetries of 3D Shapes from Single-View RGB-D ImagesPreprint

Shi, Y., Huang, J., Zhang, H., Xu, X., Rusinkiewicz, S. M., & Xu, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.00485 · Cited by 9

TD-MPC2: Scalable, Robust World Models for Continuous Control

Hansen, N., Su, H., & Wang, X. (2023) · arXiv · doi:10.48550/arxiv.2310.16828 · Cited by 8

Active-Perceptive Motion Generation for Mobile Manipulation

Jauhri, S., Lueth, S., & Chalvatzaki, G. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610714 · Cited by 8

TactGen: Tactile Sensory Data Generation via Zero-Shot Sim-to-Real Transfer

Zhong, S., Albini, A., Maiolino, P., & Posner, I. (2024) · IEEE Transactions on Robotics · doi:10.1109/tro.2024.3521967 · Cited by 8

Scalable Real2Sim: Physics-Aware Asset Generation Via Robotic Pick-and-Place Setups

Pfaff, N., Fu, E., Binagia, J. P., Isola, P., & Tedrake, R. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246653 · Cited by 8

Fuzzy-Depth Objects Grasping Based on FSG Algorithm and a Soft Robotic Hand

Cao, H., Huang, J., Li, Y., Zhou, J., & Liu, Y. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636173 · Cited by 8

DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets

Huang, I., Narang, Y., Bajcsy, R. K., Ramos, F. T., Hermans, T., & Fox, D. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160986 · Cited by 12

Multi-Object Manipulation via Object-Centric Neural Scattering Functions

Tian, S., Cai, Y., Yu, H.-X., Zakharov, S. V., Liu, K., Gaidon, A., Li, Y., & Wu, J. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00871 · Cited by 8

Viewpoint Push Planning for Mapping of Unknown Confined Spaces

Dengler, N., Pan, S., Kalagaturu, V., Menon, R., Dawood, M., & Bennewitz, M. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341809 · Cited by 8

Direct 3D model-based object tracking with event camera by motion interpolation

Kang, Y. J., Caron, G., Ishikawa, R., Escande, A., Chappellet, K., Sagawa, R., & Oishi, T. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611576 · Cited by 9

Mitigating imbalances in heterogeneous feature fusion for multi-class 6D pose estimation

Wang, H., Zhang, H., Liu, W., Lv, W., Gu, X. D., & Guo, K. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111918 · Cited by 8

CNS: Correspondence Encoded Neural Image Servo Policy

Chen, A., Yu, H., Wang, Y., & Xiong, R. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611185 · Cited by 8

A novel pipeline for bi-manual handover task

Vezzani, G., Regoli, M., Pattacini, U., & Natale, L. (2017) · Advanced Robotics · doi:10.1080/01691864.2017.1380535 · Cited by 8

Employing Magnets to Improve the Force Exertion Capabilities of Adaptive Robot Hands in Precision Grasps

Gerez, L. F., Gao, G., & Liarokapis, M. V. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967939 · Cited by 8

Object Detection and Pose Estimation Using CNN in Embedded Hardware for Assistive Technology

Demby’s, J., Gao, Y., Shafiekhani, A., & DeSouza, G. N. (2019) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci44817.2019.9002767 · Cited by 7

A 3-Stage Machine Learning-Based Novel Object Grasping Methodology

van Vuuren, J. J., Tang, L., Al-Bahadly, I. H., & Arif, K. M. (2020) · IEEE Access · doi:10.1109/access.2020.2987341 · Cited by 8

A 6DoF Pose Estimation Dataset and Network for Multiple Parametric Shapes in Stacked Scenarios

Zhang, X., Lv, W., & Zeng, L. (2021) · Machines · doi:10.3390/machines9120321 · Cited by 8

Equidistant and Uniform Data Augmentation for 3D Objects

Morozov, A., Zgyatti, D., & Popov, P. A. (2021) · IEEE Access · doi:10.1109/access.2021.3138162 · Cited by 8

SafePicking: Learning Safe Object Extraction via Object-Level Mapping

Wada, K., James, S., & Davison, A. J. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812009 · Cited by 7

Semantic Keypoint-Based Pose Estimation from Single RGB Frames

Schmeckpeper, K., Osteen, P. R., Wang, Y., Pavlakos, G., Chaney, K. P., Jordan, W., Zhou, X., Derpanis, K. G., & Daniilidis, K. (2022) · Field Robotics · doi:10.55417/fr.2022006 · Cited by 8

Multi-Finger Grasping Like Humans

Du, Y., Weinzaepfel, P., Lepetit, V., & Brégier, R. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981805 · Cited by 8

Viewpoint selection for visual failure detection

Saran, A., Lakic, B., Majumdar, S., Hess, J., & Niekum, S. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206439 · Cited by 8

Visual and Tactile Fusion for Estimating the Pose of a Grasped Object

Álvarez, D., Roa, M. A., & Moreno, L. E. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-36150-1_16 · Cited by 8

Geometric Correspondence Fields: Learned Differentiable Rendering for 3D Pose Refinement in the Wild

Grabner, A., Wang, Y., Zhang, P., Guo, P., Xiao, T., Vajda, P., Roth, P. M., & Lepetit, V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58517-4_7 · Cited by 8

Affordance-based robot object retrieval

Nguyen, T. N., Gopalan, N., Patel, R., Corsaro, M., Pavlick, E., & Tellex, S. (2021) · Autonomous Robots · doi:10.1007/s10514-021-10008-7 · Cited by 8

Semi-automatic 3D Object Keypoint Annotation and Detection for the Masses

Blomqvist, K., Chung, J. J., Ott, L., & Siegwart, R. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956263 · Cited by 8

Task and Motion Planning for Execution in the Real

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Dense Hand-Object (HO) GraspNet with Full Grasping Taxonomy and Dynamics

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