RoboBERT: An End-to-end Multimodal Robotic Manipulation Model
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916715539988480 |
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| author | Wang, Sicheng Liu, Sheng Wang, Weiheng Shan, Jianhua Fang, Bin |
| author_facet | Wang, Sicheng Liu, Sheng Wang, Weiheng Shan, Jianhua Fang, Bin |
| contents | Embodied intelligence seamlessly integrates vision, language, and action.~However, most multimodal robotic models rely on massive fine-tuning, incurring high time and hardware costs.~To address this, we introduce RoboBERT, an end-to-end multimodal manipulation model built around a novel two-stage training paradigm.~In the first stage, we freeze most of the vision encoder and train with a single "standard" instruction phrasing, allowing the model to focus on stable policy learning via a CNN-based diffusion policy.~In the second stage, we unfreeze all modules and inject diverse natural language variants, rapidly aligning varied instructions to the already-learned policy without destabilizing performance.~We further employ systematic data augmentations to enhance robustness against visual perturbations.~Without relying on auxiliary datasets, RoboBERT achieves new state-of-the-art (SOTA) mean episode lengths of 4.52 on the CALVIN ABCD-D benchmark and 3.79 on the ABC-D benchmark using only language-labeled expert demonstrations and a comparatively lightweight architecture.Real-robot trials on a 6-DOF manipulator confirm higher success rates than comparable methods trained on identical data.These results demonstrate that our data-augmentation-enhanced two-stage training paradigm delivers efficient, scalable, and broadly applicable performance for multimodal robotic systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_07837 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RoboBERT: An End-to-end Multimodal Robotic Manipulation Model Wang, Sicheng Liu, Sheng Wang, Weiheng Shan, Jianhua Fang, Bin Robotics Machine Learning Embodied intelligence seamlessly integrates vision, language, and action.~However, most multimodal robotic models rely on massive fine-tuning, incurring high time and hardware costs.~To address this, we introduce RoboBERT, an end-to-end multimodal manipulation model built around a novel two-stage training paradigm.~In the first stage, we freeze most of the vision encoder and train with a single "standard" instruction phrasing, allowing the model to focus on stable policy learning via a CNN-based diffusion policy.~In the second stage, we unfreeze all modules and inject diverse natural language variants, rapidly aligning varied instructions to the already-learned policy without destabilizing performance.~We further employ systematic data augmentations to enhance robustness against visual perturbations.~Without relying on auxiliary datasets, RoboBERT achieves new state-of-the-art (SOTA) mean episode lengths of 4.52 on the CALVIN ABCD-D benchmark and 3.79 on the ABC-D benchmark using only language-labeled expert demonstrations and a comparatively lightweight architecture.Real-robot trials on a 6-DOF manipulator confirm higher success rates than comparable methods trained on identical data.These results demonstrate that our data-augmentation-enhanced two-stage training paradigm delivers efficient, scalable, and broadly applicable performance for multimodal robotic systems. |
| title | RoboBERT: An End-to-end Multimodal Robotic Manipulation Model |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2502.07837 |