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Natural Language Can Help Bridge the Sim2Real Gap (2405.10020v2)

Published 16 May 2024 in cs.RO, cs.CL, cs.CV, and cs.LG

Abstract: The main challenge in learning image-conditioned robotic policies is acquiring a visual representation conducive to low-level control. Due to the high dimensionality of the image space, learning a good visual representation requires a considerable amount of visual data. However, when learning in the real world, data is expensive. Sim2Real is a promising paradigm for overcoming data scarcity in the real-world target domain by using a simulator to collect large amounts of cheap data closely related to the target task. However, it is difficult to transfer an image-conditioned policy from sim to real when the domains are very visually dissimilar. To bridge the sim2real visual gap, we propose using natural language descriptions of images as a unifying signal across domains that captures the underlying task-relevant semantics. Our key insight is that if two image observations from different domains are labeled with similar language, the policy should predict similar action distributions for both images. We demonstrate that training the image encoder to predict the language description or the distance between descriptions of a sim or real image serves as a useful, data-efficient pretraining step that helps learn a domain-invariant image representation. We can then use this image encoder as the backbone of an IL policy trained simultaneously on a large amount of simulated and a handful of real demonstrations. Our approach outperforms widely used prior sim2real methods and strong vision-language pretraining baselines like CLIP and R3M by 25 to 40%. See additional videos and materials at https://robin-lab.cs.utexas.edu/lang4sim2real/.

PDF HTML Abstract

Natural Language Can Help Bridge the Sim2Real Gap

Introduction

In recent years, researchers have found that visual imitation learning (IL) can successfully handle manipulation tasks in household environments. But the challenge remains in leveraging this technology in real-world scenarios where data is scarce. Albert Yu and colleagues from UT Austin tackle this problem by looking at sim2real transfer: using simulated data to fill in the gaps due to the lack of real-world data.

Their solution? Bridging the gap between simulated and real environments using natural language descriptions as the "common denominator" to capture task-relevant semantics across domains. Let’s dive into how they do it and the implications of their research.

Key Insights

The main idea here is quite elegant. If a simulation and a real-world image are described with similar language (e.g., “the robot’s gripper is right above the pan handle”), then the action distributions predicted by a learning policy should be similar. The authors propose to use pre-trained LLMs to capture these semantics effectively.

By training the image encoder to predict the description or distance between descriptions, they can achieve a domain-invariant representation. This essentially means the model can generalize from simulation to the real world without being thrown off by visual dissimilarities.

Methodology Breakdown

To understand this better, let’s look at the main steps in their approach:

  1. Visual Representation Learning: They start by pretraining an image encoder to predict language descriptions or distances between these descriptions using both simulated and real-world images. By doing so, they align similar semantic states visually across two different domains.
  2. Policy Training: The pretrained image encoder is then used as the backbone of their imitation learning (IL) policy. They train this combined setup on a large amount of simulated data and a handful of real-world demonstrations, essentially fine-tuning it for real-world applications.

The authors named their approach Lang4Sim2Real and tested it across a variety of conditions, showing significant improvement over existing sim2real methods and vision-language pretraining baselines like CLIP and R3M.

Numerical Results

In their experiments, Lang4Sim2Real significantly outperformed previously established sim2real methods and strong vision-language baselines by 25% to 40%. Such numerical results highlight the effectiveness of their approach. These improvements were demonstrated on multiple tasks, confirming the robustness of their method.

Implications and Future Directions

Practical Applications: This technique could drastically reduce the costs and efforts associated with real-world data collection, making it more feasible for businesses and researchers to deploy effective robotic solutions in dynamic environments.

Broader Impact: The method could extend beyond robotics to other fields requiring sim2real transfer, such as autonomous driving, where simulations cannot fully capture the complexities of real-world environments.

Theoretical Contributions: By demonstrating a novel way to leverage natural language for domain invariance, this work opens up new avenues for research into more sophisticated and semantically aware models.

Future Developments

Looking ahead, we can expect several advances:

  1. Enhanced Models: Integrating more advanced LLMs and experimenting with different types of semantic data could further improve domain invariance.
  2. Broader Applications: Expanding this approach to different fields and tasks could reveal hidden potentials and applications not yet considered.
  3. Automated Data Labelling: Developing methods to automatically generate suitable language descriptions for any dataset would save significant manual effort.

They have successfully shown how natural language can serve as a bridge between highly dissimilar domains, paving the way for more robust and adaptable AI systems. This paper not only adds to our understanding of domain transfer but also demonstrates a practical solution to a longstanding issue in robotic learning.

In conclusion, this work underscores the importance of interdisciplinarity in AI, where insights from natural language processing can revolutionize our approach to challenges in domains like robotics. This research is a commendable step forward in making intelligent systems more adaptable and effective in real-world settings.

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References (64)
  1. A unified approach for motion and force control of robot manipulators: The operational space formulation. IEEE Journal on Robotics and Automation, 3(1):43–53, 1987.
  2. Do as I can and not as I say: Grounding language in robotic affordances. In arXiv preprint arXiv:2204.01691, 2022.
  3. Invariance is key to generalization: Examining the role of representation in sim-to-real transfer for visual navigation. arXiv preprint arXiv:2310.15020, 2023.
  4. Learning dexterous in-hand manipulation. The International Journal of Robotics Research, 39(1):3–20, 2020.
  5. Unsupervised pixel-level domain adaptation with generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3722–3731, 2017.
  6. Rt-1: Robotics transformer for real-world control at scale. arXiv preprint arXiv:2212.06817, 2022.
  7. Rt-2: Vision-language-action models transfer web knowledge to robotic control. arXiv preprint arXiv:2307.15818, 2023.
  8. Open-vocabulary queryable scene representations for real world planning. In arXiv preprint arXiv:2209.09874, 2022.
  9. Scaling egocentric vision: The epic-kitchens dataset. In European Conference on Computer Vision (ECCV), 2018.
  10. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009.
  11. Minedojo: Building open-ended embodied agents with internet-scale knowledge. Advances in Neural Information Processing Systems, 35:18343–18362, 2022.
  12. Using natural language for reward shaping in reinforcement learning. 2019. URL https://arxiv.org/abs/1903.02020.
  13. Pixl2r: Guiding reinforcement learning using natural language by mapping pixels to rewards. 2020. URL https://arxiv.org/abs/2007.15543.
  14. The” something something” video database for learning and evaluating visual common sense. In Proceedings of the IEEE international conference on computer vision, pages 5842–5850, 2017.
  15. Ego4d: Around the world in 3,000 hours of egocentric video. arXiv preprint arXiv:2110.07058, 2021.
  16. Maskvit: Masked visual pre-training for video prediction. arXiv preprint arXiv:2206.11894, 2022.
  17. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2015.
  18. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729–9738, 2020.
  19. Retinagan: An object-aware approach to sim-to-real transfer. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 10920–10926. IEEE, 2021.
  20. Inner monologue: Embodied reasoning through planning with language models. In arXiv preprint arXiv:2207.05608, 2022.
  21. Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12627–12637, 2019.
  22. BC-z: Zero-shot task generalization with robotic imitation learning. In 5th Annual Conference on Robot Learning, 2021. URL https://openreview.net/forum?id=8kbp23tSGYv.
  23. Vima: General robot manipulation with multimodal prompts. arXiv, 2022.
  24. Exploring visual pre-training for robot manipulation: Datasets, models and methods. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 11390–11395. IEEE, 2023.
  25. Lila: Language-informed latent actions. In 5th Annual Conference on Robot Learning, 2021. URL https://arxiv.org/pdf/2111.03205.
  26. Sim2real transfer for reinforcement learning without dynamics randomization. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4383–4388. IEEE, 2020.
  27. Curl: Contrastive unsupervised representations for reinforcement learning. In International Conference on Machine Learning, pages 5639–5650. PMLR, 2020.
  28. Grounding dino: Marrying dino with grounded pre-training for open-set object detection, 2023.
  29. Language conditioned imitation learning over unstructured data. Robotics: Science and Systems, 2021. URL https://arxiv.org/abs/2005.07648.
  30. Liv: Language-image representations and rewards for robotic control. arXiv preprint arXiv:2306.00958, 2023.
  31. Sim-to-real reinforcement learning for deformable object manipulation. In Conference on Robot Learning, pages 734–743. PMLR, 2018.
  32. Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks, 2021. URL https://arxiv.org/abs/2112.03227.
  33. What matters in language conditioned imitation learning. arXiv preprint arXiv:2204.06252, 2022.
  34. Driving policy transfer via modularity and abstraction. arXiv preprint arXiv:1804.09364, 2018.
  35. Learning language-conditioned robot behavior from offline data and crowd-sourced annotation. In 5th Annual Conference on Robot Learning, 2021. URL https://arxiv.org/pdf/2109.01115.
  36. R3m: A universal visual representation for robot manipulation. arXiv preprint arXiv:2203.12601, 2022.
  37. Solving rubik’s cube with a robot hand, 2019.
  38. Open x-embodiment: Robotic learning datasets and rt-x models. arXiv preprint arXiv:2310.08864, 2023.
  39. Film: Visual reasoning with a general conditioning layer. In AAAI, 2018.
  40. Dean Pomerleau. Alvinn: An autonomous land vehicle in a neural network. In Conference on Neural Information Processing Systems (NeurIPS), 1988.
  41. Learning transferable visual models from natural language supervision. arXiv preprint arXiv:2103.00020, 2021.
  42. Real-world robot learning with masked visual pre-training. In Conference on Robot Learning, pages 416–426. PMLR, 2023.
  43. Cape: Corrective actions from precondition errors using large language models. In 2nd Workshop on Language and Robot Learning: Language as Grounding, 2023.
  44. Rl-cyclegan: Reinforcement learning aware simulation-to-real. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11157–11166, 2020.
  45. Stefan Schaal. Is imitation learning the route to humanoid robots? Trends in cognitive sciences, 3(6):233–242, 1999.
  46. Bleurt: Learning robust metrics for text generation. arXiv preprint arXiv:2004.04696, 2020.
  47. Masked world models for visual control. In Conference on Robot Learning, pages 1332–1344. PMLR, 2023.
  48. Mutex: Learning unified policies from multimodal task specifications. arXiv preprint arXiv:2309.14320, 2023.
  49. Concept2robot: Learning manipulation concepts from instructions and human demonstrations. In Proceedings of Robotics: Science and Systems (RSS), 2020.
  50. Cliport: What and where pathways for robotic manipulation. In Proceedings of the 5th Conference on Robot Learning (CoRL), 2021.
  51. Perceiver-actor: A multi-task transformer for robotic manipulation. Conference on Robot Learning, 2022.
  52. Lancon-learn: Learning with language to enable generalization in multi-task manipulation. In IEEE Robotics and Automation Letters, 2021.
  53. Multi-task reinforcement learning with context-based representations. arXiv preprint arXiv:2102.06177, 2021.
  54. Domain randomization for transferring deep neural networks from simulation to the real world. In 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS), pages 23–30. IEEE, 2017.
  55. Mujoco: A physics engine for model-based control. In 2012 IEEE/RSJ international conference on intelligent robots and systems, pages 5026–5033. IEEE, 2012.
  56. Deep domain confusion: Maximizing for domain invariance, 2014.
  57. Vrl3: A data-driven framework for visual deep reinforcement learning. Advances in Neural Information Processing Systems, 35:32974–32988, 2022.
  58. Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers, 2020.
  59. Using both demonstrations and language instructions to efficiently learn robotic tasks. arXiv preprint arXiv:2210.04476, 2022.
  60. Preparing for the unknown: Learning a universal policy with online system identification. arXiv preprint arXiv:1702.02453, 2017.
  61. Pre-trained image encoder for generalizable visual reinforcement learning. Advances in Neural Information Processing Systems, 35:13022–13037, 2022.
  62. What makes representation learning from videos hard for control? 2022. URL https://api.semanticscholar.org/CorpusID:252635608.
  63. Languagebind: Extending video-language pretraining to n-modality by language-based semantic alignment. arXiv preprint arXiv:2310.01852, 2023.
  64. robosuite: A modular simulation framework and benchmark for robot learning. arXiv preprint arXiv:2009.12293, 2020.
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Authors (4)
  1. Albert Yu (7 papers)
  2. Adeline Foote (1 paper)
  3. Raymond Mooney (21 papers)
  4. Roberto Martín-Martín (79 papers)
Citations (8)
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