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We present Neural Contact Fields, a method that brings together neural fields and tactile sensing to address the problem of tracking extrinsic contact between object and environment.
N. C. Dafle, A. Rodriguez, R. Paolini, B. Tang, S. S. Srinivasa, M. Erdmann, M. T. Mason, I. Lundberg, H. Staab, and T. Fuhlbrigge, “Extrinsic dexterity: In-hand manipulation with external forces,” in 2014 IEEE International Conference on Robotics and Automation (ICRA) , 2014, pp. 1578–1585
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H. De Vries, F. Strub, J. Mary, H. Larochelle, O. Pietquin, and A. C. Courville, “Modulating early visual processing by language,” Advances in Neural Information Processing Systems , vol. 30, 2017
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S. Wang, J. Wu, X. Sun, W. Yuan, W. T. Freeman, J. B. Tenenbaum, and E. H. Adelson, “3d shape perception from monocular vision, touch, and shape priors,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 1606–1613
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S. Dong and A. Rodriguez, “Tactile-based insertion for dense box-packing,” IEEE International Conference on Intelligent Robots and Systems , pp. 7953–7960, 9 2019
2019
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J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, “Deepsdf: Learning continuous signed distance functions for shape representation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 165–174
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L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger, “Occupancy networks: Learning 3d reconstruction in function space,” in Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2019
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M. Lambeta, P.-W. Chou, S. Tian, B. Yang, B. Maloon, V. R. Most, D. Stroud, R. Santos, A. Byagowi, G. Kammerer, et al. , “Digit: A novel design for a low-cost compact high-resolution tactile sensor with application to in-hand manipulation,” IEEE Robotics and Automation Letters , vol. 5, no. 3, pp. 3838–3845, 2020
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Y. Zhang, W. Yuan, Z. Kan, and M. Y. Wang, “Towards learning to detect and predict contact events on vision-based tactile sensors,” in Conference on Robot Learning . PMLR, 2020, pp. 1395–1404
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E. Smith, R. Calandra, A. Romero, G. Gkioxari, D. Meger, J. Malik, and M. Drozdzal, “3d shape reconstruction from vision and touch,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 14 193–14 206
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2020
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D. Ma, S. Dong, and A. Rodriguez, “Extrinsic contact sensing with relative-motion tracking from distributed tactile measurements,” in 2021 IEEE international conference on robotics and automation (ICRA) . IEEE, 2021, pp. 11 262–11 268
2021
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E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation for games, robotics and machine learning,” 2016–2021
2021
Later among the works it cites.
W. Zhou and D. Held, “Learning to grasp the ungraspable with emergent extrinsic dexterity,” in ICRA 2022 Workshop: Reinforcement Learning for Contact-Rich Manipulation , 2022
2022
Closest in time.
S. Kim and A. Rodriguez, “Active extrinsic contact sensing: Application to general peg-in-hole insertion,” in 2022 International Conference on Robotics and Automation (ICRA) , 2022, pp. 10 241–10 247
2022
Closest in time.
S. Wang, M. Lambeta, P.-W. Chou, and R. Calandra, “Tacto: A fast, flexible, and open-source simulator for high-resolution vision-based tactile sensors,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 3930–3937, 2022
2022
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M. Danielczuk, A. Mousavian, C. Eppner, and D. Fox, “Object rearrangement using learned implicit collision functions,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 6010–6017
2021
Cited alongside, same era.
S. Dong, D. K. Jha, D. Romeres, S. Kim, D. Nikovski, and A. Rodriguez, “Tactile-rl for insertion: Generalization to objects of unknown geometry,” Proceedings - IEEE International Conference on Robotics and Automation , vol. 2021-May, pp. 6437–6443, 2021
2021
Cited alongside, same era.
P. Sodhi, M. Kaess, M. Mukadam, and S. Anderson, “Learning tactile models for factor graph-based estimation,” in IEEE Intl. Conf. on Robotics and Automation (ICRA) , 2021
2021
Cited alongside, same era.
S. Suresh, M. Bauza, K.-T. Yu, J. G. Mangelson, A. Rodriguez, and M. Kaess, “Tactile slam: Real-time inference of shape and pose from planar pushing,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 11 322–11 328
2021
Cited alongside, same era.
M. B. Villalonga, A. Rodriguez, B. Lim, E. Valls, and T. Sechopoulos, “Tactile object pose estimation from the first touch with geometric contact rendering,” in Conference on Robot Learning . PMLR, 2021, pp. 1015–1029
2021
Cited alongside, same era.
A. Pumarola, E. Corona, G. Pons-Moll, and F. Moreno-Noguer, “D-nerf: Neural radiance fields for dynamic scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 10 318–10 327
2021
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K. Park, U. Sinha, J. T. Barron, S. Bouaziz, D. B. Goldman, S. M. Seitz, and R. Martin-Brualla, “Nerfies: Deformable neural radiance fields,” ICCV , 2021
2021
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C. Deng, O. Litany, Y. Duan, A. Poulenard, A. Tagliasacchi, and L. J. Guibas, “Vector neurons: A general framework for so (3)-equivariant networks,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 12 200–12 209
2021
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——, “Patchgraph: In-hand tactile tracking with learned surface normals,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 2164–2170
2022
Closest in time.
M. Bauza, A. Bronars, and A. Rodriguez, “Tac2pose: Tactile object pose estimation from the first touch,” 2022
2022
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S. Suresh, Z. Si, S. Anderson, M. Kaess, and M. Mukadam, “MidasTouch: Monte-Carlo inference over distributions across sliding touch,” in Conference on Robot Learning (CoRL) , 2022
2022
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S. Suresh, Z. Si, J. G. Mangelson, W. Yuan, and M. Kaess, “Shapemap 3-d: Efficient shape mapping through dense touch and vision,” in 2022 International Conference on Robotics and Automation (ICRA) , 2022, pp. 7073–7080
2022
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Y. Li, S. Li, V. Sitzmann, P. Agrawal, and A. Torralba, “3d neural scene representations for visuomotor control,” in Conference on Robot Learning . PMLR, 2022, pp. 112–123
2022
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B. Chen, R. Kwiatkowski, C. Vondrick, and H. Lipson, “Fully body visual self-modeling of robot morphologies,” Science Robotics , vol. 7, no. 68, p. eabn1944, 2022
2022
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J. Ortiz, A. Clegg, J. Dong, E. Sucar, D. Novotny, M. Zollhoefer, and M. Mukadam, “iSDF: Real-time neural signed distance fields for robot perception,” Robotics: Science and Systems (RSS) , 2022
2022
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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 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 6394–6400
2022
Closest in time.