Fetching the paper…
Reading the bibliography…
Learning from Demonstrations, the field that proposes to learn robot behavior models from data, is gaining popularity with the emergence of deep generative models.
Y.-C. Lin, P. Florence, A. Zeng, J. T. Barron, Y. Du, W.-C. Ma, A. Simeonov, A. R. Garcia, and P. Isola, “Mira: Mental imagery for robotic affordances,” in Conference on Robot Learning . PMLR, 2023, pp. 1916–1927
1927
Earlier work this paper cites.
O. Khatib, “A unified approach for motion and force control of robot manipulators: The operational space formulation,” IEEE Journal on Robotics and Automation , vol. 3, no. 1, pp. 43–53, 1987
1987
Earlier work this paper cites.
D. A. Pomerleau, “Alvinn: An autonomous land vehicle in a neural network,” Advances in Neural Information Processing Systems , 1988
1988
Earlier work this paper cites.
D. Q. Mayne and H. Michalska, “Receding horizon control of nonlinear systems,” in Proceedings of the 27th IEEE Conference on Decision and Control . IEEE, 1988, pp. 464–465
1988
Earlier work this paper cites.
C. M. Bishop, “Mixture density networks,” 1994
1994
Earlier work this paper cites.
S. Tso and K. Liu, “Hidden markov model for intelligent extraction of robot trajectory command from demonstrated trajectories,” in Proceedings of the IEEE International Conference on Industrial Technology (ICIT’96) . IEEE, 1996, pp. 294–298
1996
Earlier work this paper cites.
L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars, “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE transactions on Robotics and Automation , vol. 12, no. 4, pp. 566–580, 1996
1996
Earlier work this paper cites.
S. Schaal, “Learning from demonstration,” in Advances in Neural Information Processing Systems , 1997
1997
Earlier work this paper cites.
J. Yang, Y. Xu, and C. S. Chen, “Human action learning via hidden markov model,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans , vol. 27, no. 1, pp. 34–44, 1997
1997
Earlier work this paper cites.
S. LaValle, “Rapidly-exploring random trees: A new tool for path planning,” Research Report 9811 , 1998
1998
Earlier work this paper cites.
S. Schaal, “Is imitation learning the route to humanoid robots,” Trends in cognitive sciences , 1999
1999
Earlier work this paper cites.
G. E. Hinton, “Training products of experts by minimizing contrastive divergence,” Neural computation , vol. 14, no. 8, pp. 1771–1800, 2002
2002
Earlier work this paper cites.
Y. Yoon, G. N. DeSouza, and A. C. Kak, “Real-time tracking and pose estimation for industrial objects using geometric features,” in 2003 IEEE International conference on robotics and automation (cat. no. 03CH37422) , vol. 3. IEEE, 2003, pp. 3473–3478
2003
Earlier work this paper cites.
K. Grochow, S. L. Martin, A. Hertzmann, and Z. Popović, “Style-based inverse kinematics,” in ACM SIGGRAPH 2004 Papers , 2004, pp. 522–531
2004
Earlier work this paper cites.
A. P. Shon, K. Grochow, and R. P. Rao, “Robotic imitation from human motion capture using gaussian processes,” in 5th IEEE-RAS International Conference on Humanoid Robots, 2005. IEEE, 2005, pp. 129–134
2005
Earlier work this paper cites.
W. H. Kwon and S. H. Han, Receding horizon control: model predictive control for state models . Springer Science & Business Media, 2005
2005
Earlier work this paper cites.
Y. LeCun, S. Chopra, R. Hadsell, M. Ranzato, and F. Huang, “A tutorial on energy-based learning,” Predicting structured data , vol. 1, no. 0, 2006
2006
Earlier work this paper cites.
S. Calinon, F. Guenter, and A. Billard, “On learning, representing, and generalizing a task in a humanoid robot,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) , vol. 37, no. 2, pp. 286–298, 2007
2007
Earlier work this paper cites.
B. D. Ziebart, A. L. Maas, J. A. Bagnell, A. K. Dey et al. , “Maximum entropy inverse reinforcement learning.” in AAAI , 2008
2008
Earlier work this paper cites.
A. Billard, S. Calinon, R. Dillmann, and S. Schaal, “Survey: Robot programming by demonstration,” Springrer, Tech. Rep., 2008
2008
Earlier work this paper cites.
J. Nakanishi, R. Cory, M. Mistry, J. Peters, and S. Schaal, “Operational space control: A theoretical and empirical comparison,” The International Journal of Robotics Research , vol. 27, no. 6, pp. 737–757, 2008
2008
Earlier work this paper cites.
B. D. Argall, S. Chernova, M. Veloso, and B. Browning, “A survey of robot learning from demonstration,” Robotics and autonomous systems , vol. 57, no. 5, pp. 469–483, 2009
2009
Earlier work this paper cites.
N. Ratliff, M. Zucker, J. A. Bagnell, and S. Srinivasa, “Chomp: Gradient optimization techniques for efficient motion planning,” in 2009 IEEE International Conference on Robotics and Automation . IEEE, 2009, pp. 489–494
2009
Earlier work this paper cites.
J. Kober, B. Mohler, and J. Peters, “Imitation and reinforcement learning for motor primitives with perceptual coupling,” in From motor learning to interaction learning in robots . Springer, 2010, pp. 209–225
2010
Earlier work this paper cites.
S. Ross, G. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics . JMLR Workshop and Conference Proceedings, 2011, pp. 627–635
2011
Earlier work this paper cites.
S. M. Khansari-Zadeh and A. Billard, “Learning stable nonlinear dynamical systems with gaussian mixture models,” IEEE Transactions on Robotics , vol. 27, no. 5, pp. 943–957, 2011
2011
Earlier work this paper cites.
P. Vincent, “A connection between score matching and denoising autoencoders,” Neural computation , vol. 23, no. 7, pp. 1661–1674, 2011
2011
Earlier work this paper cites.
M. Kalakrishnan, S. Chitta, E. Theodorou, P. Pastor, and S. Schaal, “Stomp: Stochastic trajectory optimization for motion planning,” in IEEE international conference on robotics and automation . IEEE, 2011, pp. 4569–4574
2011
Earlier work this paper cites.
M. T. Spaan, “Partially observable markov decision processes,” in Reinforcement learning: State-of-the-art . Springer, 2012, pp. 387–414
2012
Earlier work this paper cites.
M. Kalakrishnan, P. Pastor, L. Righetti, and S. Schaal, “Learning objective functions for manipulation,” in 2013 IEEE International Conference on Robotics and Automation . IEEE, 2013, pp. 1331–1336
2013
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in Conference on Learning Representations, ICLR 2014 , Y. Bengio and Y. LeCun, Eds., 2014
2014
Earlier work this paper cites.
S. Chernova and A. L. Thomaz, Robot learning from human teachers . Morgan & Claypool Publishers, 2014
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems , 2014
2014
Earlier work this paper cites.
D. Rezende and S. Mohamed, “Variational inference with normalizing flows,” in International Conference on Machine Learning , 2015
2015
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 , 2015
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Advances in neural information processing systems , vol. 28, 2015
2015
Earlier work this paper cites.
J. Ho and S. Ermon, “Generative adversarial imitation learning,” in Advances in Neural Information Processing Systems , 2016
2016
Earlier work this paper cites.
S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” The Journal of Machine Learning Research , vol. 17, no. 1, pp. 1334–1373, 2016
2016
Earlier work this paper cites.
A. G. Billard, S. Calinon, and R. Dillmann, “Learning from humans,” Springer handbook of robotics , pp. 1995–2014, 2016
2016
Earlier work this paper cites.
H. Van Hoof, N. Chen, M. Karl, P. van der Smagt, and J. Peters, “Stable reinforcement learning with autoencoders for tactile and visual data,” in 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2016, pp. 3928–3934
2016
Earlier work this paper cites.
C. Finn, X. Y. Tan, Y. Duan, T. Darrell, S. Levine, and P. Abbeel, “Deep spatial autoencoders for visuomotor learning,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2016, pp. 512–519
2016
Earlier work this paper cites.
C. Finn, S. Levine, and P. Abbeel, “Guided cost learning: Deep inverse optimal control via policy optimization,” in International Conference on Machine Learning , 2016
2016
Earlier work this paper cites.
A. Van den Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, A. Graves et al. , “Conditional image generation with pixelcnn decoders,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
M. Mukadam, X. Yan, and B. Boots, “Gaussian process motion planning,” in 2016 IEEE international conference on robotics and automation (ICRA) . IEEE, 2016, pp. 9–15
2016
Earlier work this paper cites.
M. Laskey, J. Lee, R. Fox, A. Dragan, and K. Goldberg, “Dart: Noise injection for robust imitation learning,” in Conference on robot learning . PMLR, 2017, pp. 143–156
2017
Earlier work this paper cites.
A. Hussein, M. M. Gaber, E. Elyan, and C. Jayne, “Imitation learning: A survey of learning methods,” ACM Computing Surveys (CSUR) , vol. 50, no. 2, pp. 1–35, 2017
2017
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,” Advances in Neural Information Processing Systems , 2017
2017
Earlier work this paper cites.
A. Van Den Oord, O. Vinyals et al. , “Neural discrete representation learning,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
G. Williams, A. Aldrich, and E. A. Theodorou, “Model predictive path integral control: From theory to parallel computation,” Journal of Guidance, Control, and Dynamics , vol. 40, no. 2, pp. 344–357, 2017
2017
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969
2017
Earlier work this paper cites.
T. Osa, J. Pajarinen, G. Neumann, J. A. Bagnell, P. Abbeel, J. Peters et al. , “An algorithmic perspective on imitation learning,” Foundations and Trends® in Robotics , vol. 7, no. 1-2, pp. 1–179, 2018
2018
Earlier work this paper cites.
J. Fu, K. Luo, and S. Levine, “Learning robust rewards with adverserial inverse reinforcement learning,” in International Conference on Learning Representations , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
P. Sermanet, C. Lynch, Y. Chebotar, J. Hsu, E. Jang, S. Schaal, S. Levine, and G. Brain, “Time-contrastive networks: Self-supervised learning from video,” in 2018 IEEE international conference on robotics and automation (ICRA) . IEEE, 2018, pp. 1134–1141
2018
Earlier work this paper cites.
A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay et al. , “Roboturk: A crowdsourcing platform for robotic skill learning through imitation,” in Conference on Robot Learning . PMLR, 2018, pp. 879–893
2018
Earlier work this paper cites.
S. James, M. Bloesch, and A. J. Davison, “Task-embedded control networks for few-shot imitation learning,” in Conference on robot learning . PMLR, 2018, pp. 783–795
2018
Earlier work this paper cites.
B. Ichter, J. Harrison, and M. Pavone, “Learning sampling distributions for robot motion planning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 7087–7094
2018
Earlier work this paper cites.
A. Zeng, S. Song, S. Welker, J. Lee, A. Rodriguez, and T. 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) . IEEE, 2018, pp. 4238–4245
2018
Earlier work this paper cites.
M. Mohammadi, A. Al-Fuqaha, and J.-S. Oh, “Path planning in support of smart mobility applications using generative adversarial networks,” in IEEE International Conference on Internet of Things , 2018
2018
Earlier work this paper cites.
R. T. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, “Neural ordinary differential equations,” in Advances in Neural Information Processing Systems , 2018
2018
Earlier work this paper cites.
K. Chua, R. Calandra, R. McAllister, and S. Levine, “Deep reinforcement learning in a handful of trials using probabilistic dynamics models,” Advances in Neural Information Processing Systems , 2018
2018
Earlier work this paper cites.
C. Sahin and T.-K. Kim, “Category-level 6d object pose recovery in depth images,” in Proceedings of the European Conference on Computer Vision (ECCV) Workshops , 2018, pp. 0–0
2018
Earlier work this paper cites.
C. Devin, P. Abbeel, T. Darrell, and S. Levine, “Deep object-centric representations for generalizable robot learning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 7111–7118
2018
Earlier work this paper cites.
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) . IEEE, 2018, pp. 1606–1613
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Mousavian, C. Eppner, and D. Fox, “6-dof graspnet: Variational grasp generation for object manipulation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 2901–2910
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Mandlekar, J. Booher, M. Spero, A. Tung, A. Gupta, Y. Zhu, A. Garg, S. Savarese, and L. Fei-Fei, “Scaling robot supervision to hundreds of hours with roboturk: Robotic manipulation dataset through human reasoning and dexterity,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 1048–1055
2019
Earlier work this paper cites.
J. D. M.-W. C. Kenton and L. K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of NAACL-HLT , 2019
2019
Earlier work this paper cites.
Y. Song and S. Ermon, “Generative modeling by estimating gradients of the data distribution,” Advances in Neural Information Processing Systems , 2019
2019
Earlier work this paper cites.
M. Yan, A. Li, M. Kalakrishnan, and P. Pastor, “Learning probabilistic multi-modal actor models for vision-based robotic grasping,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 4804–4810
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Y. Du and I. Mordatch, “Implicit generation and modeling with energy based models,” Advances in Neural Information Processing Systems , 2019
2019
Earlier work this paper cites.
Y. Du, T. Lin, and I. Mordatch, “Model based planning with energy based models,” CORL , 2019
2019
Earlier work this paper cites.
L. Manuelli, W. Gao, P. Florence, and R. Tedrake, “kpam: Keypoint affordances for category-level robotic manipulation,” in The International Symposium of Robotics Research . Springer, 2019, pp. 132–157
2019
Earlier work this paper cites.
T. D. Kulkarni, A. Gupta, C. Ionescu, S. Borgeaud, M. Reynolds, A. Zisserman, and V. Mnih, “Unsupervised learning of object keypoints for perception and control,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
D. Wang, C. Devin, Q.-Z. Cai, F. Yu, and T. Darrell, “Deep object-centric policies for autonomous driving,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 8853–8859
2019
Cited alongside, same era.
2020
Cited alongside, same era.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems , 2020
2020
Cited alongside, same era.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
Later among the works it cites.
Y. Wang, R. Wu, K. Mo, J. Ke, Q. Fan, L. J. Guibas, and H. Dong, “Adaafford: Learning to adapt manipulation affordance for 3d articulated objects via few-shot interactions,” in European Conference on Computer Vision . Springer, 2022, pp. 90–107
2022
Later among the works it cites.
Y. Zhao, R. Wu, Z. Chen, Y. Zhang, Q. Fan, K. Mo, and H. Dong, “Dualafford: Learning collaborative visual affordance for dual-gripper manipulation,” in The Eleventh International Conference on Learning Representations , 2022
2022
Later among the works it cites.
K. Mo, Y. Qin, F. Xiang, H. Su, and L. Guibas, “O2o-afford: Annotation-free large-scale object-object affordance learning,” in Conference on Robot Learning . PMLR, 2022, pp. 1666–1677
2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
J. Wu, X. Sun, A. Zeng, S. Song, J. Lee, S. Rusinkiewicz, and T. Funkhouser, “Spatial action maps for mobile manipulation,” in 16th Robotics: Science and Systems, RSS 2020 . MIT Press Journals, 2020
2020
Cited alongside, same era.
H. Ravichandar, A. S. Polydoros, S. Chernova, and A. Billard, “Recent advances in robot learning from demonstration,” Annual review of control, robotics, and autonomous systems , vol. 3, pp. 297–330, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
A. Zeng, S. Song, J. Lee, A. Rodriguez, and T. Funkhouser, “Tossingbot: Learning to throw arbitrary objects with residual physics,” IEEE Transactions on Robotics , vol. 36, no. 4, pp. 1307–1319, 2020
2020
Cited alongside, same era.
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 , 2020
2020
Cited alongside, same era.
A. Mandlekar, F. Ramos, B. Boots, S. Savarese, L. Fei-Fei, A. Garg, and D. Fox, “Iris: Implicit reinforcement without interaction at scale for learning control from offline robot manipulation data,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 4414–4420
2020
Cited alongside, same era.
Later among the works it cites.
L. Fan, G. Wang, Y. Jiang, A. Mandlekar, Y. Yang, H. Zhu, A. Tang, D.-A. Huang, Y. Zhu, and A. Anandkumar, “Minedojo: Building open-ended embodied agents with internet-scale knowledge,” Advances in Neural Information Processing Systems , vol. 35, pp. 18 343–18 362, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
C. Celemin, R. Pérez-Dattari, E. Chisari, G. Franzese, L. de Souza Rosa, R. Prakash, Z. Ajanović, M. Ferraz, A. Valada, J. Kober et al. , “Interactive imitation learning in robotics: A survey,” Foundations and Trends® in Robotics , vol. 10, no. 1-2, pp. 1–197, 2022
2022
Later among the works it cites.
M. Shridhar, L. Manuelli, and D. Fox, “Perceiver-actor: A multi-task transformer for robotic manipulation,” in Conference on Robot Learning , 2023
2023
Later among the works it cites.
C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” Proceedings of Robotics: Science and Systems (R:SS) , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
W. Liu, T. Hermans, S. Chernova, and C. Paxton, “Structdiffusion: Object-centric diffusion for semantic rearrangement of novel objects,” Proceedings of Robotics: Science and Systems (R:SS) , 2023
2023
Later among the works it cites.
A. Simeonov, A. Goyal, L. Manuelli, Y.-C. Lin, A. Sarmiento, A. R. Garcia, P. Agrawal, and D. Fox, “Shelving, stacking, hanging: Relational pose diffusion for multi-modal rearrangement,” in Conference on Robot Learning , 2023
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.
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Urain, N. Funk, G. Chalvatzaki, and J. Peters, “Se(3)-diffusionfields: Learning cost functions for joint grasp and motion optimization through diffusion,” IEEE International Conference on Robotics and Automation (ICRA) , 2023
2023
Later among the works it cites.
H. Ha, P. Florence, and S. Song, “Scaling up and distilling down: Language-guided robot skill acquisition,” in Conference on Robot Learning , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
T. Weng, D. Held, F. Meier, and M. Mukadam, “Neural grasp distance fields for robot manipulation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 1814–1821
2023
Later among the works it cites.
J. Urain, A. Li, P. Liu, C. D’Eramo, and J. Peters, “Composable energy policies for reactive motion generation and reinforcement learning,” The International Journal of Robotics Research (IJRR) , 2023
2023
Later among the works it cites.
N. Gkanatsios, A. Jain, Z. Xian, Y. Zhang, C. G. Atkeson, and K. Fragkiadaki, “Energy-based models are zero-shot planners for compositional scene rearrangement,” in RSS 2023 Workshop on Learning for Task and Motion Planning , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
A. Simeonov, Y. Du, Y.-C. Lin, A. R. Garcia, L. P. Kaelbling, T. Lozano-Pérez, and P. Agrawal, “Se (3)-equivariant relational rearrangement with neural descriptor fields,” in Conference on Robot Learning . PMLR, 2023, pp. 835–846
2023
Later among the works it cites.
Y. Du, C. Durkan, R. Strudel, J. B. Tenenbaum, S. Dieleman, R. Fergus, J. Sohl-Dickstein, A. Doucet, and W. S. Grathwohl, “Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and mcmc,” in International Conference on Machine Learning . PMLR, 2023, pp. 8489–8510
2023
Later among the works it cites.
2023
Later among the works it cites.
U. Mishra, S. Xue, Y. Chen, and D. Xu, “Generative skill chaining: Long-horizon skill planning with diffusion models,” in CoRL 2023 Workshop on Learning Effective Abstractions for Planning (LEAP) , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
M. Reuss and R. Lioutikov, “Multimodal diffusion transformer for learning from play,” in 2nd Workshop on Language and Robot Learning: Language as Grounding , 2023
2023
Later among the works it cites.
S. Huang, Z. Wang, P. Li, B. Jia, T. Liu, Y. Zhu, W. Liang, and S.-C. Zhu, “Diffusion-based generation, optimization, and planning in 3d scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 16 750–16 761
2023
Later among the works it cites.
2023
Later among the works it cites.
I. Kapelyukh, V. Vosylius, and E. Johns, “Dall-e-bot: Introducing web-scale diffusion models to robotics,” IEEE Robotics and Automation Letters , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Du, S. Yang, B. Dai, H. Dai, O. Nachum, J. B. Tenenbaum, D. Schuurmans, and P. Abbeel, “Learning universal policies via text-guided video generation,” in Thirty-seventh Conference on Neural Information Processing Systems , 2023
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.
O. M. Team, D. Ghosh, H. Walke, K. Pertsch, K. Black, O. Mees, S. Dasari, J. Hejna, C. Xu, J. Luo et al. , “Octo: An open-source generalist robot policy,” 2023
2023
Later among the works it cites.
Y. Zhu, A. Joshi, P. Stone, and Y. Zhu, “Viola: Imitation learning for vision-based manipulation with object proposal priors,” in Proceedings of The 6th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, K. Liu, D. Kulic, and J. Ichnowski, Eds., vol. 205. PMLR, 14–18 Dec 2023, pp. 1199–1210. [Online]. Available: https://proceedings.mlr.press/v205/zhu23a.html
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.
Y. Ze, G. Yan, Y.-H. Wu, A. Macaluso, Y. Ge, J. Ye, N. Hansen, L. E. Li, and X. Wang, “Gnfactor: Multi-task real robot learning with generalizable neural feature fields,” in Conference on Robot Learning . PMLR, 2023, pp. 284–301
2023
Later among the works it cites.
Z. Xian, N. Gkanatsios, T. Gervet, T.-W. Ke, and K. Fragkiadaki, “Chaineddiffuser: Unifying trajectory diffusion and keypose prediction for robotic manipulation,” in 7th Annual Conference on Robot Learning , 2023
2023
Later among the works it cites.
C. Jiang, A. Cornman, C. Park, B. Sapp, Y. Zhou, D. Anguelov et al. , “Motiondiffuser: Controllable multi-agent motion prediction using diffusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9644–9653
2023
Later among the works it cites.
2023
Later among the works it cites.
W. Liu, J. Mao, J. Hsu, T. Hermans, A. Garg, and J. Wu, “Composable part-based manipulation,” in 7th Annual Conference on Robot Learning , 2023
2023
Later among the works it cites.
N. Heravi, A. Wahid, C. Lynch, P. Florence, T. Armstrong, J. Tompson, P. Sermanet, J. Bohg, and D. Dwibedi, “Visuomotor control in multi-object scenes using object-aware representations,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 9515–9522
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
C. Huang, O. Mees, A. Zeng, and W. Burgard, “Visual language maps for robot navigation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 10 608–10 615
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Chen, A. E. Tekden, M. P. Deisenroth, and Y. Bekiroglu, “Sliding touch-based exploration for modeling unknown object shape with multi-fingered hands,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 8943–8950
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Geng, B. An, H. Geng, Y. Chen, Y. Yang, and H. Dong, “Rlafford: End-to-end affordance learning for robotic manipulation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5880–5886
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
X. Jia, D. Blessing, X. Jiang, M. Reuss, A. Donat, R. Lioutikov, and G. Neumann, “Towards diverse behaviors: A benchmark for imitation learning with human demonstrations,” International Conference on Learning Representations (ICLR) , 2024
2024
Closest in time.
2024
Closest in time.
P. M. Scheikl, N. Schreiber, C. Haas, N. Freymuth, G. Neumann, R. Lioutikov, and F. Mathis-Ullrich, “Movement primitive diffusion: Learning gentle robotic manipulation of deformable objects,” IEEE Robotics and Automation Letters , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
U. A. Mishra, Y. Chen, and D. Xu, “Generative factor chaining: Coordinated manipulation with diffusion-based factor graph,” in ICRA Workshop { \{ \ \backslash textemdash } \} Back to the Future: Robot Learning Going Probabilistic , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
B. Liu, Y. Zhu, C. Gao, Y. Feng, Q. Liu, Y. Zhu, and P. Stone, “Libero: Benchmarking knowledge transfer for lifelong robot learning,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.