Fetching the paper…
Reading the bibliography…
Robots operating in human environments must be able to rearrange objects into semantically-meaningful configurations, even if these objects are previously unseen.
1908
Earlier work this paper cites.
A. Curtis, X. Fang, L. P. Kaelbling, T. Lozano-Pérez, and C. R. Garrett, “Long-horizon manipulation of unknown objects via task and motion planning with estimated affordances,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 1940–1946
1946
Earlier work this paper cites.
J. J. Kuffner and S. M. LaValle, “RRT-connect: An efficient approach to single-query path planning,” in Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065) , vol. 2. IEEE, 2000, pp. 995–1001
2000
Earlier work this paper cites.
R. B. Rusu and S. Cousins, “3d is here: Point cloud library (pcl),” in 2011 IEEE international conference on robotics and automation . IEEE, 2011, pp. 1–4
2011
Earlier work this paper cites.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International Conference on Machine Learning . PMLR, 2015, pp. 2256–2265
2015
Earlier work this paper cites.
B. Calli, A. Singh, A. Walsman, S. Srinivasa, P. Abbeel, and A. M. Dollar, “The ycb object and model set: Towards common benchmarks for manipulation research,” in 2015 international conference on advanced robotics (ICAR) . IEEE, 2015, pp. 510–517
2015
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation in robotics, games and machine learning,” 2017
2017
Earlier work this paper cites.
A. Ten Pas and R. Platt, “Using geometry to detect grasp poses in 3d point clouds,” in Robotics research . Springer, 2018, pp. 307–324
2018
Earlier work this paper cites.
D. Kent and R. Toris, “Adaptive autonomous grasp selection via pairwise ranking,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 2971–2976
2018
Earlier work this paper cites.
Y. Zhou, C. Barnes, J. Lu, J. Yang, and H. Li, “On the continuity of rotation representations in neural networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 5745–5753
2019
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems , vol. 33, pp. 6840–6851, 2020
2020
Earlier work this paper cites.
C. Lynch, M. Khansari, T. Xiao, V. Kumar, J. Tompson, S. Levine, and P. Sermanet, “Learning latent plans from play,” in Conference on robot learning . PMLR, 2020, pp. 1113–1132
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
A. Murali, A. Mousavian, C. Eppner, C. Paxton, and D. Fox, “6-dof grasping for target-driven object manipulation in clutter,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 6232–6238
2020
Earlier work this paper cites.
Q. Lu, M. Van der Merwe, B. Sundaralingam, and T. Hermans, “Multifingered grasp planning via inference in deep neural networks: Outperforming sampling by learning differentiable models,” IEEE Robotics & Automation Magazine , vol. 27, no. 2, pp. 55–65, 2020
2020
Earlier work this paper cites.
C. Paxton, C. Xie, T. Hermans, and D. Fox, “Predicting stable configurations for semantic placement of novel objects,” in Conference on Robot Learning (CoRL) , 2021
2021
Earlier work this paper cites.
A. Qureshi, A. Mousavian, C. Paxton, M. Yip, and D. Fox, “Nerp: Neural rearrangement planning for unknown objects,” in Proceedings of Robotics: Science and Systems , 2021
2021
Cited alongside, same era.
M.-H. Guo, J.-X. Cai, Z.-N. Liu, T.-J. Mu, R. R. Martin, and S.-M. Hu, “PCT: Point cloud transformer,” Computational Visual Media , vol. 7, no. 2, pp. 187–199, 2021
2021
Cited alongside, same era.
W. Yuan, C. Paxton, K. Desingh, and D. Fox, “Sornet: Spatial object-centric representations for sequential manipulation,” in 5th Annual Conference on Robot Learning . PMLR, 2021, pp. 148–157
2021
Cited alongside, same era.
P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” Advances in Neural Information Processing Systems , vol. 34, pp. 8780–8794, 2021
2021
Cited alongside, same era.
2022
Closest in time.
O. Mees, L. Hermann, and W. Burgard, “What matters in language conditioned robotic imitation learning over unstructured data,” IEEE Robotics and Automation Letters (RA-L) , vol. 7, no. 4, pp. 11 205–11 212, 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
M. Chen, S. L. Herbert, H. Hu, Y. Pu, J. F. Fisac, S. Bansal, S. Han, and C. J. Tomlin, “Fastrack: a modular framework for real-time motion planning and guaranteed safe tracking,” IEEE Transactions on Automatic Control , vol. 66, no. 12, pp. 5861–5876, 2021
2021
Cited alongside, same era.
A. Szot, A. Clegg, E. Undersander, E. Wijmans, Y. Zhao, J. Turner, N. Maestre, M. Mukadam, D. Chaplot, O. Maksymets, A. Gokaslan, V. Vondrus, S. Dharur, F. Meier, W. Galuba, A. Chang, Z. Kira, V. Koltun, J. Malik, M. Savva, and D. Batra, “Habitat 2.0: Training home assistants to rearrange their habitat,” in Advances in Neural Information Processing Systems (NeurIPS) , 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
M. Sundermeyer, A. Mousavian, R. Triebel, and D. Fox, “Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 13 438–13 444
2021
Cited alongside, same era.
2022
Cited alongside, same era.
M. Shridhar, L. Manuelli, and D. Fox, “Cliport: What and where pathways for robotic manipulation,” in Conference on Robot Learning . PMLR, 2022, pp. 894–906
2022
Cited alongside, same era.
2022
Cited alongside, same era.
W. Liu, C. Paxton, T. Hermans, and D. Fox, “Structformer: Learning spatial structure for language-guided semantic rearrangement of novel objects,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 6322–6329
2022
Cited alongside, same era.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
A. Bobu, C. Paxton, W. Yang, B. Sundaralingam, Y.-W. Chao, M. Cakmak, and D. Fox, “Learning perceptual concepts by bootstrapping from human queries,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 11 260–11 267, 2022
2022
Closest in time.
A. Simeonov, Y. Du, L. Yen-Chen, , A. Rodriguez, , L. P. Kaelbling, T. L. Perez, and P. Agrawal, “Se(3)-equivariant relational rearrangement with neural descriptor fields,” in Conference on Robot Learning (CoRL) . PMLR, 2022
2022
Closest in time.
T. Migimatsu and J. Bohg, “Grounding predicates through actions,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 3498–3504
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
S. Nair, E. Mitchell, K. Chen, S. Savarese, C. Finn et al. , “Learning language-conditioned robot behavior from offline data and crowd-sourced annotation,” in Conference on Robot Learning . PMLR, 2022, pp. 1303–1315
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
J. Zhao, D. Troniak, and O. Kroemer, “Towards robotic assembly by predicting robust, precise and task-oriented grasps,” in CoRL , 2022
2022
Closest in time.