Fetching the paper…
Reading the bibliography…
Three challenges limit the progress of robot learning research: robots are expensive (few labs can participate), everyone uses different robots (findings do not generalize across labs), and we lack internet-scale robotics data.
G. Seetharaman, A. Lakhotia, and E. P. Blasch, “Unmanned vehicles come of age: The darpa grand challenge,” Computer , vol. 39, no. 12, pp. 26–29, 2006
2006
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in Conference on Computer Vision and Pattern Recognition . IEEE, 2009, pp. 248–255
2009
Earlier work this paper cites.
M. Buehler, K. Iagnemma, and S. Singh, The DARPA urban challenge: autonomous vehicles in city traffic . Springer, 2009, vol. 56
2009
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in International Conference on Intelligent Robots and Systems . IEEE, 2012, pp. 5026–5033
2012
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 International Conference on Advanced Robotics . IEEE, 2015, pp. 510–517
2015
Earlier work this paper cites.
V. Kumar and E. Todorov, “Mujoco haptix: A virtual reality system for hand manipulation,” in International Conference on Humanoid Robots . IEEE, 2015, pp. 657–663
2015
Earlier work this paper cites.
N. Correll, K. E. Bekris, D. Berenson, O. Brock, A. Causo, K. Hauser, K. Okada, A. Rodriguez, J. M. Romano, and P. R. Wurman, “Analysis and observations from the first amazon picking challenge,” IEEE Transactions on Automation Science and Engineering , vol. 15, no. 1, pp. 172–188, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
L. Pinto and A. Gupta, “Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours,” in International Conference on Robotics and Automation . IEEE, 2016, pp. 3406–3413
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Conference on Computer Vision and Pattern Recognition . IEEE, 2016, pp. 770–778
2016
Earlier work this paper cites.
E. Krotkov, D. Hackett, L. Jackel, M. Perschbacher, J. Pippine, J. Strauss, G. Pratt, and C. Orlowski, “The darpa robotics challenge finals: Results and perspectives,” Journal of Field Robotics , vol. 34, no. 2, pp. 229–240, 2017
2017
Earlier work this paper cites.
D. Pickem, P. Glotfelter, L. Wang, M. Mote, A. Ames, E. Feron, and M. Egerstedt, “The robotarium: A remotely accessible swarm robotics research testbed,” in International Conference on Robotics and Automation . IEEE, 2017, pp. 1699–1706
2017
Earlier work this paper cites.
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
Cited alongside, same era.
S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, “Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection,” The International Journal of Robotics Research , vol. 37, no. 4-5, pp. 421–436, 2018
2018
Cited alongside, same era.
P. Sharma, L. Mohan, L. Pinto, and A. Gupta, “Multiple interactions made easy (mime): Large scale demonstrations data for imitation,” in Conference on Robot Learning . PMLR, 2018, pp. 906–915
2018
Cited alongside, same era.
E. Mansimov and K. Cho, “Simple nearest neighbor policy method for continuous control tasks,” 2018. [Online]. Available: https://openreview.net/forum?id=ByL48G-AW
2018
Cited alongside, same era.
Z. Liu, W. Liu, Y. Qin, F. Xiang, M. Gou, S. Xin, M. A. Roa, B. Calli, H. Su, Y. Sun et al. , “Ocrtoc: A cloud-based competition and benchmark for robotic grasping and manipulation,” IEEE Robotics and Automation Letters , vol. 7, no. 1, pp. 486–493, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
L. Ke, J. Wang, T. Bhattacharjee, B. Boots, and S. Srinivasa, “Grasping with chopsticks: Combating covariate shift in model-free imitation learning for fine manipulation,” in International Conference on Robotics and Automation . IEEE, 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
J. Collins, J. McVicar, D. Wedlock, R. Brown, D. Howard, and J. Leitner, “Benchmarking simulated robotic manipulation through a real world dataset,” IEEE Robotics and Automation Letters , vol. 5, no. 1, pp. 250–257, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine, “Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,” in Conference on Robot Learning . PMLR, 2020, pp. 1094–1100
2020
Cited alongside, same era.
2020
Cited alongside, same era.
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar et al. , “Bootstrap your own latent-a new approach to self-supervised learning,” Advances in Neural Information Processing Systems , vol. 33, pp. 21 271–21 284, 2020
2020
Cited alongside, same era.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Conference on Computer Vision and Pattern Recognition . IEEE, 2020, pp. 9729–9738
2020
Cited alongside, same era.
S. Dasari, J. Wang, J. Hong, S. Bahl, Y. Lin, A. S. Wang, A. Thankaraj, K. S. Chahal, B. Calli, S. Gupta et al. , “Rb2: Robotic manipulation benchmarking with a twist,” in NeurIPS Datasets and Benchmarks Track , 2021
2021
Cited alongside, same era.
2021
Later among the works it cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International Conference on Machine Learning . PMLR, 2021, pp. 8748–8763
2021
Later among the works it cites.
L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, and I. Mordatch, “Decision transformer: Reinforcement learning via sequence modeling,” Advances in Neural Information Processing Systems , vol. 34, pp. 15 084–15 097, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
V. Dean, Y. G. Shavit, and A. Gupta, “Robots on demand: A democratized robotics research cloud,” in Conference on Robot Learning . PMLR, 2022, pp. 1769–1775
2022
Later among the works it cites.
S. Parisi, A. Rajeswaran, S. Purushwalkam, and A. K. Gupta, “The unsurprising effectiveness of pre-trained vision models for control,” in ICML , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
K. Grauman, A. Westbury, E. Byrne, Z. Chavis, A. Furnari, R. Girdhar, J. Hamburger, H. Jiang, M. Liu, X. Liu et al. , “Ego4d: Around the world in 3,000 hours of egocentric video,” in Conference on Computer Vision and Pattern Recognition . IEEE, 2022, pp. 18 995–19 012
2022
Later among the works it cites.