Built on

S. Young, D. Gandhi, S. Tulsiani, A. Gupta, P. Abbeel, and L. Pinto, “Visual imitation made easy,” in Conference on Robot learning . PMLR, 2021, pp. 1992–2005
2005
Earlier work this paper cites.
B. C. Stadie, P. Abbeel, and I. Sutskever, “Third-person imitation learning,” arXiv preprint arXiv:1703.01703 , 2017
Original
2017
Earlier work this paper cites.
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska et al. , “Overcoming catastrophic forgetting in neural networks,” Proceedings of the national academy of sciences , vol. 114, no. 13, pp. 3521–3526, 2017
2017
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,” arXiv preprint arXiv:2112.01511 , 2021
Original
2021
Earlier work this paper 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
Earlier work this paper cites.
P. Schaldenbrand, J. McCann, and J. Oh, “Frida: A collaborative robot painter with a differentiable, real2sim2real planning environment,” arXiv preprint arXiv:2210.00664 , 2022
Original
2022
Earlier work this paper cites.
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn et al. , “Rt-2: Vision-language-action models transfer web knowledge to robotic control,” arXiv preprint arXiv:2307.15818 , 2023
Original
2023
Earlier work this paper cites.
M. Du, S. Nair, D. Sadigh, and C. Finn, “Behavior retrieval: Few-shot imitation learning by querying unlabeled datasets,” arXiv preprint arXiv:2304.08742 , 2023
Original
2023
Earlier work this paper cites.
A. George and A. B. Farimani, “One act play: Single demonstration behavior cloning with action chunking transformers,” arXiv preprint arXiv:2309.10175 , 2023
Original
2023
Earlier work this paper cites.

Similar

M. Oquab, T. Darcet, T. Moutakanni, H. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby et al. , “Dinov2: Learning robust visual features without supervision,” arXiv preprint arXiv:2304.07193 , 2023
Original
2023
Cited alongside, same era.
X. Zhai, B. Mustafa, A. Kolesnikov, and L. Beyer, “Sigmoid loss for language image pre-training,” in Proceedings of the IEEE/CVF international conference on computer vision , 2023, pp. 11 975–11 986
2023
Cited alongside, same era.
M. J. Kim, K. Pertsch, S. Karamcheti, T. Xiao, A. Balakrishna, S. Nair, R. Rafailov, E. Foster, G. Lam, P. Sanketi et al. , “Openvla: An open-source vision-language-action model,” arXiv preprint arXiv:2406.09246 , 2024
Original
2024
Cited alongside, same era.
K. Black, N. Brown, D. Driess, A. Esmail, M. Equi, C. Finn, N. Fusai, L. Groom, K. Hausman, B. Ichter et al. , “ π 0 \pi_{0} : A vision–language–action flow model for general robot control,” arXiv preprint arXiv:2410.24164 , 2024
Original
2024
Cited alongside, same era.

Then

A. Bartsch, A. Car, C. Avra, and A. B. Farimani, “Sculptdiff: Learning robotic clay sculpting from humans with goal conditioned diffusion policy,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2024, pp. 7307–7314
2024
Later among the works it cites.
A. O’Neill, A. Rehman, A. Maddukuri, A. Gupta, A. Padalkar, A. Lee, A. Pooley, A. Gupta, A. Mandlekar, A. Jain et al. , “Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 6892–6903
2024
Later among the works it cites.
Y. Kuang, J. Ye, H. Geng, J. Mao, C. Deng, L. Guibas, H. Wang, and Y. Wang, “Ram: Retrieval-based affordance transfer for generalizable zero-shot robotic manipulation,” arXiv preprint arXiv:2407.04689 , 2024
Original
2024
Later among the works it cites.
M. Memmel, J. Berg, B. Chen, A. Gupta, and J. Francis, “Strap: Robot sub-trajectory retrieval for augmented policy learning,” arXiv preprint arXiv:2412.15182 , 2024

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2025

RT-Cache: Training-Free Retrieval for Real-Time Manipulation

Kwon, Owen, George, Abraham, Bartsch, Alison et al.

Understand

Real robots are expected to repeat the same behavior in new environments with very little new data, yet modern controllers either incur heavy per-step inference or require deployment-time fine-tuning.

We propose RT-Cache, a training-free retrieval-as-control pipeline that caches diverse image action trajectories in a unified vector memory and, at test time, embeds the current frame to retrieve and replay multi-step snippets, replacing per-step model calls.
A hierarchical search keeps lookups sub-second at million scale, shifting cost from compute to storage and enabling real-time control on modest GPUs.
Across real-robot tasks and large open logs, RT-Cache achieves higher success and lower completion time than strong retrieval baselines (approximately x2 higher success and ~30% faster in our settings), and a single-episode anchoring study shows immediate adaptation to a more complex, contact-rich task without fine-tuning.

Built on

S. Young, D. Gandhi, S. Tulsiani, A. Gupta, P. Abbeel, and L. Pinto, “Visual imitation made easy,” in Conference on Robot learning . PMLR, 2021, pp. 1992–2005
2005
Earlier work this paper cites.
B. C. Stadie, P. Abbeel, and I. Sutskever, “Third-person imitation learning,” arXiv preprint arXiv:1703.01703 , 2017
Original
2017
Earlier work this paper cites.
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska et al. , “Overcoming catastrophic forgetting in neural networks,” Proceedings of the national academy of sciences , vol. 114, no. 13, pp. 3521–3526, 2017
2017
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,” arXiv preprint arXiv:2112.01511 , 2021
Original
2021
Earlier work this paper 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
Earlier work this paper cites.
P. Schaldenbrand, J. McCann, and J. Oh, “Frida: A collaborative robot painter with a differentiable, real2sim2real planning environment,” arXiv preprint arXiv:2210.00664 , 2022
Original
2022
Earlier work this paper cites.
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn et al. , “Rt-2: Vision-language-action models transfer web knowledge to robotic control,” arXiv preprint arXiv:2307.15818 , 2023
Original
2023
Earlier work this paper cites.
M. Du, S. Nair, D. Sadigh, and C. Finn, “Behavior retrieval: Few-shot imitation learning by querying unlabeled datasets,” arXiv preprint arXiv:2304.08742 , 2023
Original
2023
Earlier work this paper cites.
A. George and A. B. Farimani, “One act play: Single demonstration behavior cloning with action chunking transformers,” arXiv preprint arXiv:2309.10175 , 2023
Original
2023
Earlier work this paper cites.

Similar

M. Oquab, T. Darcet, T. Moutakanni, H. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby et al. , “Dinov2: Learning robust visual features without supervision,” arXiv preprint arXiv:2304.07193 , 2023
Original
2023
Cited alongside, same era.
X. Zhai, B. Mustafa, A. Kolesnikov, and L. Beyer, “Sigmoid loss for language image pre-training,” in Proceedings of the IEEE/CVF international conference on computer vision , 2023, pp. 11 975–11 986
2023
Cited alongside, same era.
M. J. Kim, K. Pertsch, S. Karamcheti, T. Xiao, A. Balakrishna, S. Nair, R. Rafailov, E. Foster, G. Lam, P. Sanketi et al. , “Openvla: An open-source vision-language-action model,” arXiv preprint arXiv:2406.09246 , 2024
Original
2024
Cited alongside, same era.
K. Black, N. Brown, D. Driess, A. Esmail, M. Equi, C. Finn, N. Fusai, L. Groom, K. Hausman, B. Ichter et al. , “ π 0 \pi_{0} : A vision–language–action flow model for general robot control,” arXiv preprint arXiv:2410.24164 , 2024
Original
2024
Cited alongside, same era.

Then

A. Bartsch, A. Car, C. Avra, and A. B. Farimani, “Sculptdiff: Learning robotic clay sculpting from humans with goal conditioned diffusion policy,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2024, pp. 7307–7314
2024
Later among the works it cites.
A. O’Neill, A. Rehman, A. Maddukuri, A. Gupta, A. Padalkar, A. Lee, A. Pooley, A. Gupta, A. Mandlekar, A. Jain et al. , “Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 6892–6903
2024
Later among the works it cites.
Y. Kuang, J. Ye, H. Geng, J. Mao, C. Deng, L. Guibas, H. Wang, and Y. Wang, “Ram: Retrieval-based affordance transfer for generalizable zero-shot robotic manipulation,” arXiv preprint arXiv:2407.04689 , 2024
Original
2024
Later among the works it cites.
M. Memmel, J. Berg, B. Chen, A. Gupta, and J. Francis, “Strap: Robot sub-trajectory retrieval for augmented policy learning,” arXiv preprint arXiv:2412.15182 , 2024

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

O. M. Team, D. Ghosh, H. Walke, K. Pertsch, K. Black, O. Mees, S. Dasari, J. Hejna, T. Kreiman, C. Xu et al. , “Octo: An open-source generalist robot policy,” arXiv preprint arXiv:2405.12213 , 2024

Original

2024

Cited alongside, same era.

L. Zha, Y. Cui, L.-H. Lin, M. Kwon, M. G. Arenas, A. Zeng, F. Xia, and D. Sadigh, “Distilling and retrieving generalizable knowledge for robot manipulation via language corrections,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 15 172–15 179

2024

Cited alongside, same era.

A. Car, S. S. Yarlagadda, A. Bartsch, A. George, and A. B. Farimani, “Plato: Planning with llms and affordances for tool manipulation,” arXiv preprint arXiv:2409.11580 , 2024

Original

2024

Cited alongside, same era.

M. Kwon, H. Hu, V. Myers, S. Karamcheti, A. Dragan, and D. Sadigh, “Toward grounded commonsense reasoning,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 5463–5470

2024

Cited alongside, same era.

A. George, S. Gano, P. Katragadda, and A. B. Farimani, “Vital pretraining: Visuo-tactile pretraining for tactile and non-tactile manipulation policies,” arXiv preprint arXiv:2403.11898 , 2024

Original

2024

Cited alongside, same era.

Original

2024

Later among the works it cites.

P. Schaldenbrand, G. Parmar, J.-Y. Zhu, J. McCann, and J. Oh, “Cofrida: Self-supervised fine-tuning for human-robot co-painting,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 2296–2302

2024

Later among the works it cites.

M. J. Kim, C. Finn, and P. Liang, “Fine-tuning vision-language-action models: Optimizing speed and success,” arXiv preprint arXiv:2502.19645 , 2025

Original

2025

Closest in time.

Y. Guo, B. Tang, I. Akinola, D. Fox, A. Gupta, and Y. Narang, “Srsa: Skill retrieval and adaptation for robotic assembly tasks,” arXiv preprint arXiv:2503.04538 , 2025

Original

2025

Closest in time.

A. Bartsch, A. Car, and A. B. Farimani, “Pinchbot: Long-horizon deformable manipulation with guided diffusion policy,” arXiv preprint arXiv:2507.17846 , 2025

Original

2025

Closest in time.