What matters in language conditioned robotic imitation learning over unstructured data
Open accessAbstract
A long-standing goal in robotics is to build robots that can perform a wide range of daily tasks from perceptions obtained with their onboard sensors and specified only via natural language. While recently substantial advances have been achieved in language-driven robotics by leveraging end-to-end learning from pixels, there is no clear and well-understood process for making various design choices due to the underlying variation in setups. In this letter, we conduct an extensive study of the most critical challenges in learning language conditioned policies from offline free-form imitation datasets. We further identify architectural and algorithmic techniques that improve performance, such as a hierarchical decomposition of the robot control learning, a multimodal transformer encoder, discrete latent plans and a self-supervised contrastive loss that aligns video and language representations. By combining the results of our investigation with our improved model components, we are able to present a novel approach that significantly outperforms the state of the art on the challenging language conditioned long-horizon robot manipulation CALVIN benchmark. We have open-sourced our implementation to facilitate future research in learning to perform many complex manipulation skills in a row specified with natural language.
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Prior transformer-based policy designs typically encode input images with large ResNet-style encoders and fuse the resulting image features with a comparatively small transformer.
“Prior transformer-based policy designs typically encode input images with large ResNetstyle [32] encoders and fuse the resulting image features with a comparatively small transformer [10, 67, 81, 17, 98, 61, 83].”
From Octo: An Open-Source Generalist Robot Policy (2024) · cites this paper as [61]
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Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Harnad, S. (1990) · Physica D: Nonlinear Phenomena · doi:10.1016/0167-2789(90)90087-6 · Cited by 4,235 · cited 3× · see citing works
“However, learning to follow language instructions involves addressing a difficult symbol grounding problem [3], relating a language”
BackgroundInfluentialCLIPort: What and Where Pathways for Robotic Manipulation
Shridhar, M., Manuelli, L., & Fox, D. (2021) · arXiv · doi:10.48550/arxiv.2109.12098 · Cited by 223 · see citing works
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
Yarats, D., Fergus, R., Lazaric, A., & Pinto, L. (2021) · arXiv · doi:10.48550/arxiv.2107.09645 · Cited by 89 · see citing works
Flagged first, then by citations
Research works printed but not linked: 29
- C. Lynch, M. Khansari, T. Xiao, V. Kumar, J. Tompson, S. Levine, and P. Sermanet, "Learning latent plans from play," in CoRL, 2019.
- D. Kalashnikov, J. Varley, Y. Chebotar, B. Swanson, R. Jonschkowski, C. Finn, S. Levine, and K. Hausman, "MT-Opt: Continuous Multi- Task Robotic Reinforcement Learning at Scale," arXiv preprint arXiv:2104.08212, 2021. · arXiv
- L. P. Kaelbling, "Learning to achieve goals," in IJCAI, 1993, pp. 1094- 1099.
- S. Stepputtis, J. Campbell, M. Phielipp, S. Lee, C. Baral, and H. B. Amor, "Language-Conditioned Imitation Learning for Robot Manipula- tion Tasks," in NeurIPS, 2020.
- E. Jang, A. Irpan, M. Khansari, D. Kappler, F. Ebert, C. Lynch, S. Levine, and C. Finn, "BC-0: Zero-Shot Task Generalization with Robotic Imitation Learning," in CoRL, 2021.
- D. I. A. Team, J. Abramson, A. Ahuja, A. Brussee, F. Carnevale, M. Cassin, F. Fischer, P. Georgiev, A. Goldin, T. Harley, et al., "Cre- ating Multimodal Interactive Agents with Imitation and Self-Supervised Learning," arXiv preprint arXiv:2112.03763, 2021. · arXiv
- S. Nair, E. Mitchell, K. Chen, B. Ichter, S. Savarese, and C. Finn, "Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation," in CoRL, 2021.
- O. Mees, L. Hermann, E. Rosete-Beas, and W. Burgard, "Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks," IEEE Robotics and Automation Letters (RA- L), vol. 7, no. 3, pp. 7327-7334, 2022.
- M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, P. Abbeel, and W. Zaremba, "Hindsight experience replay," in NeurIPS, 2017.
- S. Tellex, N. Gopalan, H. Kress-Gazit, and C. Matuszek, "Robots that use language," Annual Review of Control, Robotics, and Autonomous Systems, vol. 3, pp. 25-55, 2020.
- J. Lu, D. Batra, D. Parikh, and S. Lee, "Vilbert: Pretraining task- agnostic visiolinguistic representations for vision-and-language tasks," in NeurIPS, 2019.
- A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al., "Learning transfer- able visual models from natural language supervision," arXiv preprint arXiv:2103.00020, 2021. · arXiv
- T. Winograd, "Understanding natural language," Cognitive psychology, vol. 3, no. 1, pp. 1-191, 1972.
- J. Hatori, Y. Kikuchi, S. Kobayashi, K. Takahashi, Y. Tsuboi, Y. Unno, W. Ko, and J. Tan, "Interactively picking real-world objects with uncon- strained spoken language instructions," in ICRA, 2018.
- W. Liu, C. Paxton, T. Hermans, and D. Fox, "StructFormer: Learning Spatial Structure for Language-Guided Semantic Rearrangement of Novel Objects," arXiv preprint arXiv:2110.10189, 2021. · arXiv
- D. P. Kingma and M. Welling, "Auto-encoding variational bayes," arXiv preprint arXiv:1312.6114, 2013. · arXiv
- J. Borja-Diaz, O. Mees, G. Kalweit, L. Hermann, J. Boedecker, and W. Burgard, "Affordance learning from play for sample-efficient policy learning," in ICRA, 2022.
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, "Attention is all you need," NeurIPS, 2017.
- D. Hafner, T. P. Lillicrap, M. Norouzi, and J. Ba, "Mastering atari with discrete world models," in ICLR, 2020.
- A. Van Den Oord, O. Vinyals, et al., "Neural discrete representation learning," NeurIPS, 2017.
- Y. Bengio, N. Léonard, and A. Courville, "Estimating or propagating gradients through stochastic neurons for conditional computation," arXiv preprint arXiv:1308.3432, 2013. · arXiv
- T. Salimans, A. Karpathy, X. Chen, and D. P. Kingma, "Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications," arXiv preprint arXiv:1701.05517, 2017. · arXiv
- S. Dasari and A. Gupta, "Transformers for one-shot visual imitation," in Conference on Robot Learning. PMLR, 2021, pp. 2071-2084.
- S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio, "Generating sentences from a continuous space," arXiv preprint arXiv:1511.06349, 2015. · arXiv
- N. Reimers and I. Gurevych, "Sentence-bert: Sentence embeddings using siamese bert-networks," in EMNLP, 2019.
- K. Song, X. Tan, T. Qin, J. Lu, and T.-Y. Liu, "Mpnet: Masked and permuted pre-training for language understanding," NeurIPS, 2020.
- Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, "Roberta: A robustly optimized bert pretraining approach," arXiv preprint arXiv:1907.11692, 2019. · arXiv
- P. de Haan, D. Jayaraman, and S. Levine, "Causal confusion in imitation learning," NeurIPS, 2019.
- O. Mees, M. Merklinger, G. Kalweit, and W. Burgard, "Adversarial skill networks: Unsupervised robot skill learning from videos," in ICRA, 2020.
Cited by
Sorted by citations
Visual Language Maps for Robot Navigation
Huang, C., Mees, O., Zeng, A., & Burgard, W. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48891.2023.10160969 · Cited by 376 · see citing works
Self-citationRT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., Finn, C., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.15818 · Cited by 276 · see citing works
“…to novel object instances [31, 34, 32, 35, 36], to tasks involving novel combinations of objects and skills [37, 38, 39, 40, 41], to new goals or language instructions [42, 43, 41, 44, 45, 46, 47], to tasks with novel semantic object categories [48, 49], and to unseen environments [50, 51, 52].”
BackgroundEmbodied large language models enable robots to complete complex tasks in unpredictable environments
Mon-Williams, R., Li, G., Long, R., Du, W., & Lucas, C. G. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01005-x · Cited by 132 · see citing works
Octo: An Open-Source Generalist Robot Policy
Ghosh, D., Walke, H., Pertsch, K., Black, K., Mees, O., Dasari, S., Hejna, J., Kreiman, T., Xu, C., Luo, J., et al. (2024) · Robotics: Science and Systems XX · doi:10.15607/rss.2024.xx.090 · Cited by 129 · see citing works
Self-citationRT-H: Action Hierarchies Using Language
Belkhale, S., Ding, T., Xiao, T., Sermanet, P., Vuong, Q., Tompson, J., Chebotar, Y., Dwibedi, D., & Sadigh, D. (2024) · arXiv · doi:10.48550/arxiv.2403.01823 · Cited by 71
“Several works have explored learning general “skill” representations as parameterized primitives [17, 18] or embeddings to describe short sequences of actions or interactions with objects, often from diverse multi-task datasets [10, 19–28].”
BackgroundScaling Up and Distilling Down: Language-Guided Robot Skill Acquisition
Ha, H., Florence, P., & Song, S. (2023) · arXiv · doi:10.48550/arxiv.2307.14535 · Cited by 24 · see citing works
- Authors
- Mees, O., Hermann, L., & Burgard, W.
- Affiliations
- University of Freiburg (DE)
- University of Freiburg, Freiburg im Breisgau, Germany
- University of Technology Nuremberg (DE)
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2022-10
- Volume
- 7
- Issue
- 4
- Pages
- 11205-11212
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W4293498911
- Cited by
- 77
- References
- 4
- Citations by year
- 2026: 14 · 2025: 35 · 2024: 16 · 2023: 11 · 2022: 1
- FWCI
- 6.34
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Robotics and Automation Letters (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- German Federal Ministry of Education and Research (01IS18040B-OML)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- contrastive learning
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