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In this work, we focus on addressing the long-horizon manipulation tasks in densely cluttered scenes.
W. Masson, P. Ranchod, and G. D. Konidaris, “Reinforcement learning with parameterized actions,” in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA , D. Schuurmans and M. P. Wellman, Eds. AAAI Press, 2016, pp. 1934–1940
1940
Earlier work this paper cites.
R. S. Sutton, D. Precup, and S. Singh, “Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning,” Artif. Intell. , vol. 112, no. 1-2, pp. 181–211, 1999
1999
Earlier work this paper cites.
M. M. Botvinick, Y. Niv, and A. G. Barto, “Hierarchically organized behavior and its neural foundations: A reinforcement learning perspective,” cognition , vol. 113, no. 3, pp. 262–280, 2009
2009
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015 . IEEE Computer Society, 2015, pp. 3431–3440
2015
Earlier work this paper cites.
M. V. Butz and E. F. Kutter, How the mind comes into being: Introducing cognitive science from a functional and computational perspective . Oxford University Press, 2016
2016
Earlier work this paper cites.
T. Schaul, J. Quan, I. Antonoglou, and D. Silver, “Prioritized experience replay,” in 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings , Y. Bengio and Y. LeCun, Eds., 2016
2016
Earlier work this paper cites.
A. Zeng, S. Song, S. Welker, J. Lee, A. Rodriguez, and T. A. Funkhouser, “Learning synergies between pushing and grasping with self-supervised deep reinforcement learning,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018, Madrid, Spain, October 1-5, 2018 . IEEE, 2018, pp. 4238–4245
2018
Earlier work this paper cites.
O. Nachum, S. Gu, H. Lee, and S. Levine, “Data-efficient hierarchical reinforcement learning,” in Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada , S. Bengio, H. M. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Eds., 2018, pp. 3307–3317
2018
Earlier work this paper cites.
Y. Deng, X. Guo, Y. Wei, K. Lu, B. Fang, D. Guo, H. Liu, and F. Sun, “Deep reinforcement learning for robotic pushing and picking in cluttered environment,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019, Macau, SAR, China, November 3-8, 2019 . IEEE, 2019, pp. 619–626
2019
Earlier work this paper cites.
Y. Su, L. Yu, H. Wang, S. Lu, P. Ser, W. Hsu, W. Lai, B. Xie, H. Huang, T. Lee, and H. Chen, “Pose-aware placement of objects with semantic labels - brandname-based affordance prediction and cooperative dual-arm active manipulation,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019, Macau, SAR, China, November 3-8, 2019 . IEEE, 2019, pp. 4760–4767
2019
Earlier work this paper cites.
A. Levy, G. D. Konidaris, R. P. Jr., and K. Saenko, “Learning multi-level hierarchies with hindsight,” in 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 . OpenReview.net, 2019
2019
Earlier work this paper cites.
A. Zeng, P. Florence, J. Tompson, S. Welker, J. Chien, M. Attarian, T. Armstrong, I. Krasin, D. Duong, V. Sindhwani, and J. Lee, “Transporter networks: Rearranging the visual world for robotic manipulation,” in 4th Conference on Robot Learning, CoRL 2020, 16-18 November 2020, Virtual Event / Cambridge, MA, USA , ser. Proceedings of Machine Learning Research, J. Kober, F. Ramos, and C. J. Tomlin, Eds., vol. 155. PMLR, 2020, pp. 726–747
2020
Earlier work this paper cites.
R. Strudel, A. Pashevich, I. Kalevatykh, I. Laptev, J. Sivic, and C. Schmid, “Learning to combine primitive skills: A step towards versatile robotic manipulation §,” in 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31 - August 31, 2020 . IEEE, 2020, pp. 4637–4643
2020
Earlier work this paper cites.
P. Abolghasemi and L. Bölöni, “Accept synthetic objects as real: End-to-end training of attentive deep visuomotor policies for manipulation in clutter,” in 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31 - August 31, 2020 . IEEE, 2020, pp. 6506–6512
2020
Earlier work this paper cites.
R. Papallas and M. R. Dogar, “Non-prehensile manipulation in clutter with human-in-the-loop,” in 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31 - August 31, 2020 . IEEE, 2020, pp. 6723–6729
2020
Cited alongside, same era.
L. Berscheid, P. Meißner, and T. Kröger, “Self-supervised learning for precise pick-and-place without object model,” IEEE Robotics Autom. Lett. , vol. 5, no. 3, pp. 4828–4835, 2020
2020
Cited alongside, same era.
S. Cheong, B. Y. Cho, J. Lee, C. Kim, and C. Nam, “Where to relocate?: Object rearrangement inside cluttered and confined environments for robotic manipulation,” in 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31 - August 31, 2020 . IEEE, 2020, pp. 7791–7797
2020
Cited alongside, same era.
C. Nam, J. Lee, S. Cheong, B. Y. Cho, and C. Kim, “Fast and resilient manipulation planning for target retrieval in clutter,” in 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31 - August 31, 2020 . IEEE, 2020, pp. 3777–3783
C. Li, R. Zhang, J. Wong, C. Gokmen, S. Srivastava, R. Martín-Martín, C. Wang, G. Levine, M. Lingelbach, J. Sun, M. Anvari, M. Hwang, M. Sharma, A. Aydin, D. Bansal, S. Hunter, K. Kim, A. Lou, C. R. Matthews, I. Villa-Renteria, J. H. Tang, C. Tang, F. Xia, S. Savarese, H. Gweon, K. Liu, J. Wu, and L. Fei-Fei, “BEHAVIOR-1K: A benchmark for embodied AI with 1, 000 everyday activities and realistic simulation,” in Conference on Robot Learning, CoRL 2022, 14-18 December 2022, Auckland, New Zealand , ser. Proceedings of Machine Learning Research, K. Liu, D. Kulic, and J. Ichnowski, Eds., vol. 205. PMLR, 2022, pp. 80–93
2022
Later among the works it cites.
S. Nasiriany, H. Liu, and Y. Zhu, “Augmenting reinforcement learning with behavior primitives for diverse manipulation tasks,” in 2022 International Conference on Robotics and Automation, ICRA 2022, Philadelphia, PA, USA, May 23-27, 2022 . IEEE, 2022, pp. 7477–7484
2022
Later among the works it cites.
S. Kumra, S. Joshi, and F. Sahin, “Learning robotic manipulation tasks via task progress based gaussian reward and loss adjusted exploration,” IEEE Robotics Autom. Lett. , vol. 7, no. 1, pp. 534–541, 2022
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2020
Cited alongside, same era.
R. Li, A. Jabri, T. Darrell, and P. Agrawal, “Towards practical multi-object manipulation using relational reinforcement learning,” in 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31 - August 31, 2020 . IEEE, 2020, pp. 4051–4058
2020
Cited alongside, same era.
Y. Lee, J. Yang, and J. J. Lim, “Learning to coordinate manipulation skills via skill behavior diversification,” in 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net, 2020
2020
Cited alongside, same era.
R. Wang, R. Yu, B. An, and Z. Rabinovich, “I 2 hrl: Interactive influence-based hierarchical reinforcement learning,” in Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020 , C. Bessiere, Ed. ijcai.org, 2020, pp. 3131–3138
2020
Cited alongside, same era.
S. Kumra, S. Joshi, and F. Sahin, “Antipodal robotic grasping using generative residual convolutional neural network,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020, Las Vegas, NV, USA, October 24, 2020 - January 24, 2021 . IEEE, 2020, pp. 9626–9633
2020
Cited alongside, same era.
M. Shridhar, L. Manuelli, and D. Fox, “Cliport: What and where pathways for robotic manipulation,” in Conference on Robot Learning, 8-11 November 2021, London, UK , ser. Proceedings of Machine Learning Research, A. Faust, D. Hsu, and G. Neumann, Eds., vol. 164. PMLR, 2021, pp. 894–906
2021
Cited alongside, same era.
B. Tang, M. Corsaro, G. Konidaris, S. Nikolaidis, and S. Tellex, “Learning collaborative pushing and grasping policies in dense clutter,” in IEEE International Conference on Robotics and Automation, ICRA 2021, Xi’an, China, May 30 - June 5, 2021 . IEEE, 2021, pp. 6177–6184
2021
Cited alongside, same era.
W. Zhao and W. Chen, “Hierarchical POMDP planning for object manipulation in clutter,” Robotics Auton. Syst. , vol. 139, p. 103736, 2021
2021
Cited alongside, same era.
J. Lee, C. Nam, J. Park, and C. Kim, “Tree search-based task and motion planning with prehensile and non-prehensile manipulation for obstacle rearrangement in clutter,” in IEEE International Conference on Robotics and Automation, ICRA 2021, Xi’an, China, May 30 - June 5, 2021 . IEEE, 2021, pp. 8516–8522
2021
Cited alongside, same era.
2022
Later among the works it cites.
S. Kumra, S. Joshi, and F. Sahin, “Learning multi-step robotic manipulation policies from visual observation of scene and q-value predictions of previous action,” in 2022 International Conference on Robotics and Automation, ICRA 2022, Philadelphia, PA, USA, May 23-27, 2022 . IEEE, 2022, pp. 8245–8251
2022
Later among the works it cites.
B. Tang and G. S. Sukhatme, “Selective object rearrangement in clutter,” in Conference on Robot Learning, CoRL 2022, 14-18 December 2022, Auckland, New Zealand , ser. Proceedings of Machine Learning Research, K. Liu, D. Kulic, and J. Ichnowski, Eds., vol. 205. PMLR, 2022, pp. 1001–1010
2022
Later among the works it cites.
X. Ying and M. C. Chuah, “Uctnet: Uncertainty-aware cross-modal transformer network for indoor RGB-D semantic segmentation,” in Computer Vision - ECCV 2022 - 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXX , ser. Lecture Notes in Computer Science, S. Avidan, G. J. Brostow, M. Cissé, G. M. Farinella, and T. Hassner, Eds., vol. 13690. Springer, 2022, pp. 20–37
2022
Later among the works it cites.
H. Huang, D. Wang, R. Walters, and R. Platt, “Equivariant transporter network,” in Robotics: Science and Systems XVIII, New York City, NY, USA, June 27 - July 1, 2022 , K. Hauser, D. A. Shell, and S. Huang, Eds., 2022
2022
Later among the works it cites.
M. H. Lim, A. Zeng, B. Ichter, M. Bandari, E. Coumans, C. J. Tomlin, S. Schaal, and A. Faust, “Multi-task learning with sequence-conditioned transporter networks,” in 2022 International Conference on Robotics and Automation, ICRA 2022, Philadelphia, PA, USA, May 23-27, 2022 . IEEE, 2022, pp. 2489–2496
2022
Later among the works it cites.
M. Eppe, C. Gumbsch, M. Kerzel, P. D. H. Nguyen, M. V. Butz, and S. Wermter, “Intelligent problem-solving as integrated hierarchical reinforcement learning,” Nat. Mach. Intell. , vol. 4, no. 1, pp. 11–20, 2022
2022
Later among the works it cites.
J. Lee and J. Choi, “Hierarchical primitive composition: Simultaneous activation of skills with inconsistent action dimensions in multiple hierarchies,” IEEE Robotics Autom. Lett. , vol. 7, no. 3, pp. 7581–7588, 2022
2022
Later among the works it cites.
X. Yang, Z. Ji, J. Wu, Y. Lai, C. Wei, G. Liu, and R. Setchi, “Hierarchical reinforcement learning with universal policies for multistep robotic manipulation,” IEEE Trans. Neural Networks Learn. Syst. , vol. 33, no. 9, pp. 4727–4741, 2022
2022
Later among the works it cites.
M. B. Imtiaz, Y. Qiao, and B. Lee, “Prehensile and non-prehensile robotic pick-and-place of objects in clutter using deep reinforcement learning,” Sensors , vol. 23, no. 3, p. 1513, 2023
2023
Closest in time.
G. Liu, J. D. Winter, D. Steckelmacher, R. K. Hota, A. Nowé, and B. Vanderborght, “Synergistic task and motion planning with reinforcement learning-based non-prehensile actions,” IEEE Robotics Autom. Lett. , vol. 8, no. 5, pp. 2764–2771, 2023
2023
Closest in time.
G. Sóti, X. Huang, C. Wurll, and B. Hein, “Train what you know - precise pick-and-place with transporter networks,” in IEEE International Conference on Robotics and Automation, ICRA 2023, London, UK, May 29 - June 2, 2023 . IEEE, 2023, pp. 5814–5820
2023
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