vMF-Contact: Uncertainty-aware Evidential Learning for Probabilistic Contact-grasp in Noisy Clutter

K. V. Mardia and S. El-Atoum, “Bayesian inference for the von mises-fisher distribution,” Biometrika , vol. 63, no. 1, pp. 203–206, 1976

1976

Earlier work this paper cites.

G. Nunez-Antonio and E. Gutiérrez-Pena, “A bayesian analysis of directional data using the von mises–fisher distribution,” Communications in Statistics—Simulation and Computation® , vol. 34, no. 4, pp. 989–999, 2005

2005

Earlier work this paper cites.

B. P. Carlin and T. A. Louis, Bayesian methods for data analysis . CRC press, 2008

2008

Earlier work this paper cites.

C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra, “Weight uncertainty in neural network,” in International conference on machine learning . PMLR, 2015, pp. 1613–1622

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.

Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in international conference on machine learning . PMLR, 2016, pp. 1050–1059

2016

Earlier work this paper cites.

I. Osband, C. Blundell, A. Pritzel, and B. Van Roy, “Deep exploration via bootstrapped dqn,” Advances in neural information processing systems , vol. 29, 2016

2016

Earlier work this paper cites.

A. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?” Advances in neural information processing systems , vol. 30, 2017

2017

Earlier work this paper cites.

B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles,” Advances in neural information processing systems , vol. 30, 2017

2017

Earlier work this paper cites.

M. A. Hasnat, J. Bohné, J. Milgram, S. Gentric, and L. Chen, “von mises-fisher mixture model-based deep learning: Application to face verification,” arXiv preprint arXiv:1706.04264 , 2017

Original

2017

Earlier work this paper cites.

M. Sensoy, L. Kaplan, and M. Kandemir, “Evidential deep learning to quantify classification uncertainty,” Advances in neural information processing systems , vol. 31, 2018

2018

Earlier work this paper cites.

Y. Yang, C. Feng, Y. Shen, and D. Tian, “Foldingnet: Point cloud auto-encoder via deep grid deformation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 206–215

2018

Earlier work this paper cites.

E. Ilg, O. Cicek, S. Galesso, A. Klein, O. Makansi, F. Hutter, and T. Brox, “Uncertainty estimates and multi-hypotheses networks for optical flow,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 652–667

2018

Earlier work this paper cites.

R. T. Chen, J. Behrmann, D. K. Duvenaud, and J.-H. Jacobsen, “Residual flows for invertible generative modeling,” Advances in Neural Information Processing Systems , vol. 32, 2019

2019

Earlier work this paper cites.

H.-S. Fang, C. Wang, M. Gou, and C. Lu, “Graspnet-1billion: A large-scale benchmark for general object grasping,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 444–11 453

2020

Earlier work this paper cites.

B. Charpentier, D. Zügner, and S. Günnemann, “Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts,” Advances in neural information processing systems , vol. 33, pp. 1356–1367, 2020

2020

Cited alongside, same era.

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, 2020, pp. 3619–3625

2020

Cited alongside, same era.

N. De Cao and W. Aziz, “The power spherical distribution,” arXiv preprint arXiv:2006.04437 , 2020

Original

2020

Cited alongside, same era.

M. Breyer, J. J. Chung, L. Ott, R. Siegwart, and J. Nieto, “Volumetric grasping network: Real-time 6 dof grasp detection in clutter,” in Conference on Robot Learning . PMLR, 2021, pp. 1602–1611

2021

Cited alongside, same era.

R. Newbury, M. Gu, L. Chumbley, A. Mousavian, C. Eppner, J. Leitner, J. Bohg, A. Morales, T. Asfour, D. Kragic et al. , “Deep learning approaches to grasp synthesis: A review,” IEEE Transactions on Robotics , vol. 39, no. 5, pp. 3994–4015, 2023

2023

Later among the works it cites.

S. Jauhri, I. Lunawat, and G. Chalvatzaki, “Learning any-view 6dof robotic grasping in cluttered scenes via neural surface rendering,” arXiv preprint arXiv:2306.07392 , 2023

Original

2023

Later among the works it cites.

Q. Dai, Y. Zhu, Y. Geng, C. Ruan, J. Zhang, and H. Wang, “Graspnerf: Multiview-based 6-dof grasp detection for transparent and specular objects using generalizable nerf,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 1757–1763

2023

Later among the works it cites.

J. Feng, J. Lee, S. Geisler, S. Gunnemann, and R. Triebel, “Topology-matching normalizing flows for out-of-distribution detection in robot learning,” arXiv preprint arXiv:2311.06481 , 2023

Original