Fetching the paper…
Reading the bibliography…
Recent advances in imitation learning have shown great promise for developing robust robot manipulation policies from demonstrations.
T. Z. Zhao, J. Tompson, D. Driess, P. Florence, S. K. S. Ghasemipour, C. Finn, and A. Wahid, “Aloha unleashed: A simple recipe for robot dexterity,” in Conference on Robot Learning , Munich, Germany, 2024, pp. 1910–1924
1924
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 , Zurich, Switzerland, 2018, pp. 879–893
2018
Earlier work this paper cites.
P. Praveena, G. Subramani, B. Mutlu, and M. Gleicher, “Characterizing input methods for human-to-robot demonstrations,” in ACM/IEEE International Conference on Human-Robot Interaction , Daegu, Korea, 2019, pp. 344–353
2019
Earlier work this paper cites.
S. Song, A. Zeng, J. Lee, and T. Funkhouser, “Grasping in the Wild: Learning 6 DoF Closed-Loop Grasping From Low-Cost Demonstrations,” vol. 5, no. 3, 2020, pp. 4978–4985
2020
Earlier work this paper cites.
J. Pari, N. M. Shafiullah, S. P. Arunachalam, and L. Pinto, “The surprising effectiveness of representation learning for visual imitation,” in Robotics: Science and Systems , Manchester, UK, 2021
2021
Earlier work this paper cites.
C. Campos, R. Elvira, J. J. G. Rodríguez, J. M. M. Montiel, and J. D. Tardós, “ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM,” IEEE Transactions on Robotics , vol. 37, no. 6, pp. 1874–1890, 2021
2021
Earlier work this paper cites.
N. M. Shafiullah, Z. Cui, A. A. Altanzaya, and L. Pinto, “Behavior transformers: Cloning k k modes with one stone,” in Advances in neural information processing systems , vol. 35, New Orleans, USA, 2022, pp. 22 955–22 968
2022
Earlier work this paper cites.
C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” in Robotics: Science and Systems , Daegu, Republic of Korea, 2023
2023
Earlier work this paper cites.
T. Z. Zhao, V. Kumar, S. Levine, and C. Finn, “Learning fine-grained bimanual manipulation with low-cost hardware,” in Robotics: Science and Systems , Daegu, Republic of Korea, 2023
2023
Earlier work this paper cites.
N. M. M. Shafiullah, A. Rai, H. Etukuru, Y. Liu, I. Misra, S. Chintala, and L. Pinto, “On bringing robots home,” arXiv preprint , 2023
2023
Earlier work this paper cites.
Y. Qin, W. Yang, B. Huang, K. Van Wyk, H. Su, X. Wang, Y.-W. Chao, and D. Fox, “Anyteleop: A general vision-based dexterous robot arm-hand teleoperation system,” in Robotics: Science and Systems , Daegu, Republic of Korea, 2023
2023
Earlier work this paper cites.
J. Duan, Y. R. Wang, M. Shridhar, D. Fox, and R. Krishna, “AR2-D2: Training a Robot Without a Robot,” in Conference on Robot Learning , Atlanta, USA, 2023, pp. 2838–2848
2023
Cited alongside, same era.
S. Lee, Y. Wang, H. Etukuru, H. J. Kim, N. M. M. Shafiullah, and L. Pinto, “Behavior generation with latent actions,” in International Conference on Machine Learning , Vienna, Austria, 2024, pp. 26 991–27 008
2024
Cited alongside, same era.
P. Wu, Y. Shentu, Z. Yi, X. Lin, and P. Abbeel, “Gello: A general, low-cost, and intuitive teleoperation framework for robot manipulators,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , Abu Dhabi, UAE, 2024, pp. 12 156–12 163
2024
Cited alongside, same era.
C. Chi, Z. Xu, C. Pan, E. Cousineau, B. Burchfiel, S. Feng, R. Tedrake, and S. Song, “Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots,” in Robotics: Science and Systems , Daegu, Republic of Korea, 2024
2024
C. Wang, H. Shi, W. Wang, R. Zhang, L. Fei-Fei, and C. K. Liu, “Dexcap: Scalable and portable mocap data collection system for dexterous manipulation,” in Robotics: Science and Systems , Delft, Netherlands, 2024
2024
Later among the works it cites.
A. Khazatsky, K. Pertsch, S. Nair, A. Balakrishna, S. Dasari, S. Karamcheti, S. Nasiriany, M. K. Srirama, L. Y. Chen, K. Ellis et al. , “DROID: A large-scale in-the-wild robot manipulation dataset,” in Robotics: Science and Systems , Delft, Netherlands, 2024
2024
Later among the works it cites.
M. Lepert, R. Doshi, and J. Bohg, “Shadow: Leveraging segmentation masks for cross-embodiment policy transfer,” in Conference on Robot Learning , Munich, Germany, 2024
2024
Later among the works it cites.
L. Y. Chen, K. Hari, K. Dharmarajan, C. Xu, Q. Vuong, and K. Goldberg, “Mirage: Cross-embodiment zero-shot policy transfer with cross-painting,” in Robotics: Science and Systems , Delft, Netherlands, 2024
2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Z. Wu, T. Wang, C. Guan, Z. Jia, S. Liang, H. Song, D. Qu, D. Wang, Z. Wang, N. Cao et al. , “Fast-UMI: A Scalable and Hardware-Independent Universal Manipulation Interface,” arXiv preprint , 2024
2024
Cited alongside, same era.
S. Dass, W. Ai, Y. Jiang, S. Singh, J. Hu, R. Zhang, P. Stone, B. Abbatematteo, and R. Martín-Martín, “TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation,” in RSS Workshop: Data Generation for Robotics , Delft, Netherlands, 2024
2024
Cited alongside, same era.
O. Rayyan, “MuJoCoAR: Phone teleoperation for robots,” Sep. 2024. [Online]. Available: https://github.com/omarrayyann/mujocoar
2024
Cited alongside, same era.
S. Yang, M. Liu, Y. Qin, R. Ding, J. Li, X. Cheng, R. Yang, S. Yi, and X. Wang, “ACE: A Cross-platform and visual-Exoskeletons System for Low-Cost Dexterous Teleoperation,” in Conference on Robot Learning , Munich, Germany, 2024, pp. 4895–4911
2024
Cited alongside, same era.
Z. Fu, T. Z. Zhao, and C. Finn, “Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation,” in Conference on Robot Learning , Munich, Germany, 2024
2024
Cited alongside, same era.
H. Ha, Y. Gao, Z. Fu, J. Tan, and S. Song, “UMI-on-Legs: Making Manipulation Policies Mobile with Manipulation-Centric Whole-body Controllers,” in Conference on Robot Learning , Munich, Germany, 2024, pp. 5254–5270
2024
Cited alongside, same era.
H. Bharadhwaj, R. Mottaghi, A. Gupta, and S. Tulsiani, “Track2act: Predicting point tracks from internet videos enables generalizable robot manipulation,” in European Conference on Computer Vision , Milan, Italy, 2024, pp. 306–324
2024
Later among the works it cites.
M. Xu, Z. Xu, Y. Xu, C. Chi, G. Wetzstein, M. Veloso, and S. Song, “Flow as the cross-domain manipulation interface,” in Conference on Robot Learning , Munich, Germany, 2024, pp. 2475–2499
2024
Later among the works it cites.
N. Ravi, V. Gabeur, Y.-T. Hu, R. Hu, C. Ryali, T. Ma, H. Khedr, R. Rädle, C. Rolland, L. Gustafson et al. , “SAM 2: Segment Anything in Images and Videos,” in International Conference on Learning Representations , 2024
2024
Later among the works it cites.
F. Lin, Y. Hu, P. Sheng, C. Wen, J. You, and Y. Gao, “Data scaling laws in imitation learning for robotic manipulation,” in International Conference on Learning Representations , Singapore, 2025
2025
Closest in time.
M. Seo, H. A. Park, S. Yuan, Y. Zhu, and L. Sentis, “Legato: Cross-embodiment imitation using a grasping tool,” IEEE Robotics and Automation Letters , vol. 10, no. 3, pp. 2854–2861, 2025
2025
Closest in time.
H. Etukuru, N. Naka, Z. Hu, S. Lee, J. Mehu, A. Edsinger, C. Paxton, S. Chintala, L. Pinto, and N. M. M. Shafiullah, “Robot utility models: General policies for zero-shot deployment in new environments,” in IEEE International Conference on Robotics and Automation , Atlanta, USA, 2025, pp. 8275–8283
2025
Closest in time.