Fetching the paper…
Reading the bibliography…
Robotic manipulation is essential for the widespread adoption of robots in industrial and home settings and has long been a focus within the robotics community.
B. D. Argall, S. Chernova, M. Veloso, and B. Browning, “A survey of robot learning from demonstration,” Robotics and Autonomous Systems , vol. 57, no. 5, pp. 469–483, 5 2009
2009
Earlier work this paper cites.
2012
Earlier work this paper cites.
L. Roveda, N. Iannacci, F. Vicentini, N. Pedrocchi, F. Braghin, and L. M. Tosatti, “Optimal Impedance Force-Tracking Control Design With Impact Formulation for Interaction Tasks,” IEEE Robotics and Automation Letters , vol. 1, no. 1, pp. 130–136, 1 2016
2016
Earlier work this paper cites.
Z. Li, Z. Huang, W. He, and C. Y. Su, “Adaptive impedance control for an upper limb robotic exoskeleton using biological signals,” IEEE Transactions on Industrial Electronics , vol. 64, no. 2, pp. 1664–1674, 2 2017
2017
Earlier work this paper cites.
T. Zhang, Z. McCarthy, O. Jow, D. Lee, X. Chen, K. Goldberg, and P. Abbeel, “Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 5 2018, pp. 5628–5635
2018
Earlier work this paper cites.
R. Rahmatizadeh, P. Abolghasemi, L. Boloni, and S. Levine, “Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-to-End Learning from Demonstration,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 5 2018, pp. 3758–3765
2018
Earlier work this paper cites.
H. Liang, X. Ma, S. Li, M. Gorner, S. Tang, B. Fang, F. Sun, and J. Zhang, “PointNetGPD: Detecting Grasp Configurations from Point Sets,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 5 2019, pp. 3629–3635
2019
Earlier work this paper cites.
P. Song, Y. Yu, and X. Zhang, “A Tutorial Survey and Comparison of Impedance Control on Robotic Manipulation,” Robotica , vol. 37, no. 5, pp. 801–836, 5 2019
2019
Earlier work this paper cites.
P. Ni, W. Zhang, X. Zhu, and Q. Cao, “PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Clouds,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 5 2020, pp. 3619–3625
2020
Earlier work this paper cites.
L. Shao, F. Ferreira, M. Jorda, V. Nambiar, J. Luo, E. Solowjow, J. A. Ojea, O. Khatib, and J. Bohg, “UniGrasp: Learning a Unified Model to Grasp With Multifingered Robotic Hands,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 2286–2293, 4 2020
2020
Earlier work this paper cites.
Z. Qin, K. Fang, Y. Zhu, L. Fei-Fei, and S. Savarese, “KETO: Learning Keypoint Representations for Tool Manipulation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 5 2020, pp. 7278–7285
2020
Earlier work this paper cites.
J. Cui and J. Trinkle, “Toward next-generation learned robot manipulation,” Sci. Robot , vol. 6, p. 9461, 2021
2021
Earlier work this paper cites.
M. Shridhar, L. Manuelli, and D. Fox, “CLIPORT: What and Where Pathways for Robotic Manipulation,” in Conference on Robot Learning (CoRL) , 2021
2021
Cited alongside, same era.
E. Johns, “Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 4613–4619
2021
Cited alongside, same era.
B. Zhao, H. Zhang, X. Lan, H. Wang, Z. Tian, and N. Zheng, “REGNet: REgion-based Grasp Network for End-to-end Grasp Detection in Point Clouds,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 5 2021, pp. 13 474–13 480
2021
Cited alongside, same era.
J. Hua, L. Zeng, G. Li, and Z. Ju, “Learning for a robot: Deep reinforcement learning, imitation learning, transfer learning,” pp. 1–21, 2 2021
2021
Cited alongside, same era.
K. Li, D. Chappell, and N. Rojas, “Immersive Demonstrations are the Key to Imitation Learning,” in Proceedings - IEEE International Conference on Robotics and Automation , vol. 2023-May. Institute of Electrical and Electronics Engineers Inc., 2023, pp. 5071–5077
2023
Later among the works it cites.
T. Z. Zhao, V. Kumar, S. Levine, and C. Finn, “Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware,” in Robotics: Science and Systems , 2023
2023
Later among the works it cites.
H. Kim, Y. Ohmura, A. Nagakubo, and Y. Kuniyoshi, “Training Robots Without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer,” IEEE Robotics and Automation Letters , vol. 8, no. 5, pp. 2906–2913, 5 2023
2023
Later among the works it cites.
T. Kwon, N. Di Palo, and E. Johns, “Language Models as Zero-Shot Trajectory Generators,” IEEE Robotics and Automation Letters , vol. 9, no. 7, pp. 6728–6735, 7 2024
2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
H. Kim, Y. Ohmura, and Y. Kuniyoshi, “Transformer-based deep imitation learning for dual-arm robot manipulation,” in IEEE International Conference on Intelligent Robots and Systems . Institute of Electrical and Electronics Engineers Inc., 2021, pp. 8965–8972
2021
Cited alongside, same era.
K. Li, N. Baron, X. Zhang, and N. Rojas, “EfficientGrasp: A Unified Data-Efficient Learning to Grasp Method for Multi-Fingered Robot Hands,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 8619–8626, 10 2022
2022
Cited alongside, same era.
Y.-H. Wu, J. Wang, and X. Wang, “Learning Generalizable Dexterous Manipulation from Human Grasp Affordance Learned Policy Visualization on Unseen Test Objects Affordance Demonstrations for Training Object Human Grasp Robot Grasp Object Human Grasp Robot Grasp Object Policy Visualization,” in Conference on Robot Learning (CoRL) , 2022
2022
Cited alongside, same era.
S. Yu, D.-H. Zhai, Y. Xia, H. Wu, and J. Liao, “SE-ResUNet: A Novel Robotic Grasp Detection Method,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 5238–5245, 4 2022
2022
Cited alongside, same era.
M. Suomalainen, Y. Karayiannidis, and V. Kyrki, “A survey of robot manipulation in contact,” Robotics and Autonomous Systems , vol. 156, 10 2022
2022
Cited alongside, same era.
Y. Jiang, A. Gupta, Z. Zhang, G. Wang, Y. Dou, Y. Chen, L. Fei-Fei, A. Anandkumar, Y. Zhu, and L. Fan, “VIMA: General Robot Manipulation with Multimodal Prompts,” in International Conference on Machine Learning , 2023
2023
Cited alongside, same era.
S. Belkhale, Y. Cui, and D. Sadigh, “HYDRA: Hybrid Robot Actions for Imitation Learning,” in Conference on Robot Learning (CoRL) , 2023
2023
Cited alongside, same era.
B. An, Y. Geng, K. Chen, X. Li, Q. Dou, and H. Dong, “RGBManip: Monocular Image-based Robotic Manipulation through Active Object Pose Estimation,” in IEEE International Conference on Robotics and Automation , 2024
2024
Closest in time.
J. Duan, W. Yuan, W. Pumacay, Y. R. Wang, K. Ehsani, D. Fox, and R. Krishna, “Manipulate-Anything: Automating Real-World Robots using Vision-Language Models,” in Conference on Robot Learning (CoRL) , 2024
2024
Closest in time.
A. Xie, L. Lee, T. Xiao, and C. Finn, “Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation,” in IEEE International Conference on Robotics and Automation (ICRA) , 2024
2024
Closest in time.
W. Chen and N. Rojas, “TraKDis: A Transformer-Based Knowledge Distillation Approach for Visual Reinforcement Learning with Application to Cloth Manipulation,” IEEE Robotics and Automation Letters , vol. 9, no. 3, pp. 2455–2462, 3 2024
2024
Closest in time.
F. Zipeng, Z. Z. Tony, and F. Chelsea, “Mobile ALOHA: Learning Bimanual Mobile Manipulation using Low-Cost Whole-Body Teleoperation PLEASE CHECK THE SUPPLEMENTARY MATERIAL FOR REAL-WORLD,” in Conference on Robot Learning (CoRL) , 2024
2024
Closest in time.
2024
Closest in time.