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An object handover between a robot and a human is a coordinated action which is prone to failure for reasons such as miscommunication, incorrect actions and unexpected object properties.
S. Parastegari, E. Noohi, B. Abbasi, and M. Žefran, “A fail-safe object handover controller,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2016, pp. 2003–2008
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E. C. Grigore, K. Eder, A. G. Pipe, C. Melhuish, and U. Leonards, “Joint Action Understanding improves Robot-to-Human Object Handover,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2013, pp. 4622–4629
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A. G. Eguiluz, I. Rañó, S. A. Coleman, and T. M. McGinnity, “Reliable object handover through tactile force sensing and effort control in the Shadow Robot hand,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2017, pp. 372–377
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J. Carreira and A. Zisserman, “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 6299–6308
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C. Lea, M. D. Flynn, R. Vidal, A. Reiter, and G. D. Hager, “Temporal Convolutional Networks for Action Segmentation and Detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 156–165
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S. Parastegari, E. Noohi, B. Abbasi, and M. Žefran, “Failure Recovery in Robot–Human Object Handover,” IEEE Transactions on Robotics , vol. 34, no. 3, pp. 660–673, 2018
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M.-J. Davari, M. Hegedus, K. Gupta, and M. Mehrandezh, “Identifying Multiple Interaction Events from Tactile Data during Robot-Human Object Transfer,” in 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) . IEEE, 2019, pp. 1–6
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Y. A. Farha and J. Gall, “MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 3575–3584
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P. Rosenberger, A. Cosgun, R. Newbury, J. Kwan, V. Ortenzi, P. Corke, and M. Grafinger, “Object-Independent Human-to-Robot Handovers Using Real Time Robotic Vision,” IEEE Robotics and Automation Letters , vol. 6, no. 1, pp. 17–23, 2020
2020
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W. Yang, C. Paxton, A. Mousavian, Y.-W. Chao, M. Cakmak, and D. Fox, “Reactive Human-to-Robot Handovers of Arbitrary Objects,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 3118–3124
2021
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Y. L. Pang, A. Xompero, C. Oh, and A. Cavallaro, “Towards safe human-to-robot handovers of unknown containers,” in 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) . IEEE, 2021, pp. 51–58
2021
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S. Thoduka and N. Hochgeschwender, “Benchmarking Robots by Inducing Failures in Competition Scenarios,” in Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. AI, Product and Service: 12th International Conference, DHM 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings, Part II . Springer, 2021, pp. 263–276
M. Mavsar and A. Ude, “RoverNet: Vision-Based Adaptive Human-to-Robot Object Handovers,” in 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) . IEEE, 2022, pp. 858–864
2022
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Z. Xu, S. Escalera, A. Pavão, M. Richard, W.-W. Tu, Q. Yao, H. Zhao, and I. Guyon, “Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform,” Patterns , vol. 3, no. 7, p. 100543, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2666389922001465
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C. Meng, T. Zhang, and T. lun Lam, “Fast and Comfortable Interactive Robot-to-Human Object Handover,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 3701–3706
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M. Mohandes, B. Moradi, K. Gupta, and M. Mehrandezh, “Robot to Human Object Handover Using Vision and Joint Torque Sensor Modalities,” in International Conference on Robot Intelligence Technology and Applications . Springer, 2022, pp. 109–124
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2021
Cited alongside, same era.
V. Ortenzi, A. Cosgun, T. Pardi, W. P. Chan, E. Croft, and D. Kulić, “Object Handovers: a Review for Robotics,” IEEE Transactions on Robotics , vol. 37, no. 6, pp. 1855–1873, 2021
2021
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I. Mamaev, D. Kretsch, H. Alagi, and B. Hein, “Grasp Detection for Robot to Human Handovers Using Capacitive Sensors,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 12 552–12 558
2021
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F. Yi, H. Wen, and T. Jiang, “ASFormer: Transformer for Action Segmentation,” British Machine Vision Conference (BMVC) , 2021
2021
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2022
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2022
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Y.-W. Chao, C. Paxton, Y. Xiang, W. Yang, B. Sundaralingam, T. Chen, A. Murali, M. Cakmak, and D. Fox, “HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 6941–6947
2022
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F. Iori, G. Perovic, F. Cini, A. Mazzeo, E. Falotico, and M. Controzzi, “DMP-Based Reactive Robot-to-Human Handover in Perturbed Scenarios,” International Journal of Social Robotics , pp. 1–16, 2023
2023
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S. Li, Y. A. Farha, Y. Liu, M.-M. Cheng, and J. Gall, “MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 6, pp. 6647–6658, 2023
2023
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G. Ding, F. Sener, and A. Yao, “Temporal Action Segmentation: An Analysis of Modern Techniques,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
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