Fetching the paper…
Reading the bibliography…
Visual imitation learning has achieved impressive progress in learning unimanual manipulation tasks from a small set of visual observations, thanks to the latest advances in computer vision.
Y. Guiard, “Asymmetric Division of Labor in Human Skilled Bimanual Action: The Kinematic Chain as a Model,” Journal of motor behavior , vol. 19, no. 4, pp. 486–517, 1987
1987
Earlier work this paper cites.
M. Muhlig, M. Gienger, J. J. Steil, and C. Goerick, “Automatic selection of task spaces for imitation learning,” in IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS) , 2009, pp. 4996–5002
2009
Earlier work this paper cites.
M. Muhlig, M. Gienger, S. Hellbach, J. J. Steil, and C. Goerick, “Task-level imitation learning using variance-based movement optimization,” in IEEE Intl. Conf. on Robotics and Automation (ICRA) , 2009, pp. 1177–1184
2009
Earlier work this paper cites.
M. Kimmerle, C. L. Ferre, K. A. Kotwica, and G. F. Michel, “Development of role-differentiated bimanual manipulation during the infant’s first year,” Developmental Psychobiology: The Journal of the International Society for Developmental Psychobiology , vol. 52, no. 2, pp. 168–180, 2010
2010
Earlier work this paper cites.
A. Ajoudani, N. G. Tsagarakis, J. Lee, M. Gabiccini, and A. Bicchi, “Natural redundancy resolution in dual-arm manipulation using configuration dependent stiffness (CDS) control,” in IEEE Intl. Conf. on Robotics and Automation (ICRA) , 2014, pp. 1480–1486
2014
Earlier work this paper cites.
H. A. Park and C. S. G. Lee, “Extended Cooperative Task Space for manipulation tasks of humanoid robots,” in IEEE Intl. Conf. on Robotics and Automation (ICRA) , 2015, pp. 6088–6093
2015
Earlier work this paper cites.
J. Lee and P. H. Chang, “Redundancy resolution for dual-arm robots inspired by human asymmetric bimanual action: Formulation and experiments,” in IEEE Intl. Conf. on Robotics and Automation (ICRA) , 2015, pp. 6058–6065
2015
Earlier work this paper cites.
Y. Zhou, M. Do, and T. Asfour, “Coordinate change dynamic movement primitives - a leader-follower approach,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2016, p. 5481–5488
2016
Earlier work this paper cites.
X. Zhao, R. Hu, P. Guerrero, N. Mitra, and T. Komura, “Relationship templates for creating scene variations,” ACM Transactions on Graphics , vol. 35, pp. 1–13, 2016
2016
Earlier work this paper cites.
S. Savic, M. Rakovic, B. Borovac, and M. Nikolic, “Hybrid motion control of humanoid robot for leader-follower cooperative tasks,” Thermal Science , vol. 20, pp. 549–561, 2016
2016
Earlier work this paper cites.
J. Romero, D. Tzionas, and M. J. Black, “Embodied hands: Modeling and capturing hands and bodies together,” ACM Transactions on Graphics , vol. 36, pp. 1–17, 2017
2017
Earlier work this paper cites.
L. P. Ureche and A. Billard, “Constraints extraction from asymmetrical bimanual tasks and their use in coordinated behavior,” Robotics and Autonomous Systems , vol. 103, pp. 222–235, 2018
2018
Earlier work this paper cites.
P. Florence, L. Manuelli, and R. Tedrake, “Dense Object Nets: Learning dense visual object descriptors by and for robotic manipulation,” in Conference on Robot Learning (CoRL) , 2018, pp. 373–385
2018
Earlier work this paper cites.
S. S. Mirrazavi Salehian, N. Figueroa, and A. Billard, “A unified framework for coordinated multi-arm motion planning,” The International Journal of Robotics Research , vol. 37, pp. 1205–1232, 2018
2018
Earlier work this paper cites.
J. Gao, Y. Zhou, and T. Asfour, “Projected Force-Admittance Control for Compliant Bimanual Tasks,” in IEEE/RAS Intl. Conf. on Humanoid Robots (Humanoids) , 2018, pp. 607–613
2018
Earlier work this paper cites.
H.-C. Lin, J. Smith, K. K. Babarahmati, N. Dehio, and M. Mistry, “A projected inverse dynamics approach for multi-arm cartesian impedance control,” in IEEE Intl. Conf. on Robotics and Automation (ICRA) , 2018, pp. 5421–5428
2018
Earlier work this paper cites.
Y. Liu, Z. Shen, Z. Lin, S. Peng, H. Bao, and X. Zhou, “GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs,” in Neural Information Processing Systems (NeurIPS) , 2019, pp. 6990–7001
2019
Earlier work this paper cites.
D. Almeida and Y. Karayiannidis, “A Lyapunov-Based Approach to Exploit Asymmetries in Robotic Dual-Arm Task Resolution,” in 2019 IEEE 58th Conference on Decision and Control (CDC) , 2019, pp. 4252–4258
2019
Earlier work this paper cites.
F. Amadio, A. Colome, and C. Torras, “Exploiting Symmetries in Reinforcement Learning of Bimanual Robotic Tasks,” IEEE Robotics and Automation Letters , vol. 4, pp. 1838–1845, 2019
2019
Earlier work this paper cites.
È. Pairet, P. Ardón, M. Mistry, and Y. Petillot, “Learning and Composing Primitive Skills for Dual-arm Manipulation,” in Towards Autonomous Robotic Systems - 20th Annual Conference (TAROS) , vol. 11649, 2019, pp. 65–77
2019
Earlier work this paper cites.
Y. Zhou, J. Gao, and T. Asfour, “Learning via-point movement primitives with inter- and extrapolation capabilities,” in IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS) , 2019, pp. 4301–4308
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
T. Asfour, M. Wächter, L. Kaul, S. Rader, P. Weiner, S. Ottenhaus, R. Grimm, Y. Zhou, M. Grotz, and F. Paus, “ARMAR-6: A high-performance humanoid for human-robot collaboration in real world scenarios,” IEEE Robotics and Automation Magazine , vol. 26, no. 4, pp. 108–121, 2019
2019
Cited alongside, same era.
P. Sundaresan, J. Grannen, B. Thananjeyan, A. Balakrishna, M. Laskey, K. Stone, J. E. Gonzalez, and K. Goldberg, “Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data,” in IEEE Intl. Conf. on Robotics and Automation (ICRA) , 2020, pp. 9411–9418
2020
Cited alongside, same era.
U. Deekshith, N. Gajjar, M. Schwarz, and S. Behnke, “Visual Descriptor Learning from Monocular Video:,” in Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications , Valletta, Malta, 2020, pp. 444–451
2020
Cited alongside, same era.
P. Florence, L. Manuelli, and R. Tedrake, “Self-supervised correspondence in visuomotor policy learning,” IEEE Robotics and Automation Letters , vol. 5, pp. 492–499, 2020
A. Simeonov, Y. Du, A. Tagliasacchi, J. B. Tenenbaum, A. Rodriguez, P. Agrawal, and V. Sitzmann, “Neural Descriptor Fields: SE(3)-equivariant object representations for manipulation,” in IEEE Intl. Conf. on Robotics and Automation (ICRA) , 2022, pp. 6394–6400
2022
Later among the works it cites.
A. Simeonov, Y. Du, L. Yen-Chen, A. Rodriguez, L. P. Kaelbling, T. Lozano-Perez, and P. Agrawal, “SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields,” in Conference on Robot Learning (CoRL) , 2022, pp. 835–846
2022
Later among the works it cites.
Z. Dong, Z. Li, Y. Yan, S. Calinon, and F. Chen, “Passive Bimanual Skills Learning From Demonstration With Motion Graph Attention Networks,” IEEE Robotics and Automation Letters , vol. 7, pp. 4917–4923, 2022
2022
Later among the works it cites.
Y. Chen, T. Wu, S. Wang, X. Feng, J. Jiang, S. M. McAleer, H. Dong, Z. Lu, S.-C. Zhu, and Y. Yang, “Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning,” in Neural Information Processing Systems (NeurIPS) , vol. 35, 2022, pp. 5150–5163
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
J. Jin, L. Petrich, M. Dehghan, and M. Jagersand, “A Geometric Perspective on Visual Imitation Learning,” in IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS) , 2020, pp. 5194–5200
2020
Cited alongside, same era.
F. Xie and A. Chowdhury, “Deep Imitation Learning for Bimanual Robotic Manipulation,” in Neural Information Processing Systems (NeurIPS) , vol. 33, 2020, pp. 2327–2337
2020
Cited alongside, same era.
C. R. G. Dreher, M. Wächter, and T. Asfour, “Learning object-action relations from bimanual human demonstration using graph networks,” IEEE Robotics and Automation Letters (RA-L) , vol. 5, no. 1, pp. 187–194, 2020
2020
Cited alongside, same era.
Z. Teed and J. Deng, “RAFT: Recurrent All-Pairs Field Transforms for Optical Flow,” in Euro. Conf. on Computer Vision (ECCV) , 2020, pp. 402–419
2020
Cited alongside, same era.
W. Gao and R. Tedrake, “kPAM 2.0: Feedback control for category-level robotic manipulation,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 2962–2969, 2021
2021
Cited alongside, same era.
M. Knaust and D. Koert, “Guided Robot Skill Learning: A User-Study on Learning Probabilistic Movement Primitives with Non-Experts,” in IEEE/RAS Intl. Conf. on Humanoid Robots (Humanoids) , 2021, pp. 514–521
2021
Cited alongside, same era.
——, “Transformer-based deep imitation learning for dual-arm robot manipulation,” in IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS) , 2021, pp. 8965–8972
2021
Cited alongside, same era.
K. Meng and A. Eloyan, “Principal manifold estimation via model complexity selection,” Journal of the Royal Statistical Society. Series B, Statistical methodology , vol. 83, no. 2, pp. 369–394, 2021
2021
Cited alongside, same era.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
T. Müller, A. Evans, C. Schied, and A. Keller, “Instant Neural Graphics Primitives with a Multiresolution Hash Encoding,” ACM Trans. Graph. , vol. 41, pp. 102:1–102:15, 2022
2022
Later among the works it cites.
E. Shahriari, S. A. B. Birjandi, and S. Haddadin, “Passivity-based adaptive force-impedance control for modular multi-manual object manipulation,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 2194–2201, 2022
2022
Later among the works it cites.
J. Gao, Z. Tao, N. Jaquier, and T. Asfour, “K-VIL: Keypoints-Based Visual Imitation Learning,” IEEE Transactions on Robotics , vol. 39, no. 5, pp. 3888–3908, 2023
2023
Later among the works it cites.
E. Chun, Y. Du, A. Simeonov, T. Lozano-Perez, and L. Kaelbling, “Local Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation,” in IEEE Intl. Conf. on Robotics and Automation (ICRA) , 2023, pp. 1830–1836
2023
Later among the works it cites.
Z. Huang, J. Xu, S. Dai, K. Xu, H. Zhang, H. Huang, and R. Hu, “NIFT: Neural Interaction Field and Template for Object Manipulation,” in IEEE Intl. Conf. on Robotics and Automation (ICRA) , 2023, pp. 1875–1881
2023
Later among the works it cites.
2023
Later among the works it cites.
G. Franzese, L. d. S. Rosa, T. Verburg, L. Peternel, and J. Kober, “Interactive Imitation Learning of Bimanual Movement Primitives,” IEEE/ASME Transactions on Mechatronics , pp. 1–13, 2023
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 (R:SS) , 2023
2023
Later among the works it cites.
H. Xu, J. Zhang, J. Cai, H. Rezatofighi, F. Yu, D. Tao, and A. Geiger, “Unifying Flow, Stereo and Depth Estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , pp. 1–18, 2023
2023
Later among the works it cites.
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollár, and R. Girshick, “Segment Anything,” in Intl. Conf. on Computer Vision (ICCV) , 2023, pp. 3992–4003
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Lin, A. Zeng, H. Wang, L. Zhang, and Y. Li, “One-Stage 3D Whole-Body Mesh Recovery With Component Aware Transformer,” in Conf. on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 21 159–21 168
2023
Later among the works it cites.