Tenma: Robust Cross-Embodiment Robot Manipulation with Diffusion Transformer
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912587667472384 |
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| author | Davies, Travis Huang, Yiqi Liu, Yunxin Chen, Xiang Liu, Huxian Hu, Luhui |
| author_facet | Davies, Travis Huang, Yiqi Liu, Yunxin Chen, Xiang Liu, Huxian Hu, Luhui |
| contents | Scaling Transformer policies and diffusion models has advanced robotic manipulation, yet combining these techniques in lightweight, cross-embodiment learning settings remains challenging. We study design choices that most affect stability and performance for diffusion-transformer policies trained on heterogeneous, multimodal robot data, and introduce Tenma, a lightweight diffusion-transformer for bi-manual arm control. Tenma integrates multiview RGB, proprioception, and language via a cross-embodiment normalizer that maps disparate state/action spaces into a shared latent space; a Joint State-Time encoder for temporally aligned observation learning with inference speed boosts; and a diffusion action decoder optimized for training stability and learning capacity. Across benchmarks and under matched compute, Tenma achieves an average success rate of 88.95% in-distribution and maintains strong performance under object and scene shifts, substantially exceeding baseline policies whose best in-distribution average is 18.12%. Despite using moderate data scale, Tenma delivers robust manipulation and generalization, indicating the great potential for multimodal and cross-embodiment learning strategies for further augmenting the capacity of transformer-based imitation learning policies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11865 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tenma: Robust Cross-Embodiment Robot Manipulation with Diffusion Transformer Davies, Travis Huang, Yiqi Liu, Yunxin Chen, Xiang Liu, Huxian Hu, Luhui Robotics Artificial Intelligence Scaling Transformer policies and diffusion models has advanced robotic manipulation, yet combining these techniques in lightweight, cross-embodiment learning settings remains challenging. We study design choices that most affect stability and performance for diffusion-transformer policies trained on heterogeneous, multimodal robot data, and introduce Tenma, a lightweight diffusion-transformer for bi-manual arm control. Tenma integrates multiview RGB, proprioception, and language via a cross-embodiment normalizer that maps disparate state/action spaces into a shared latent space; a Joint State-Time encoder for temporally aligned observation learning with inference speed boosts; and a diffusion action decoder optimized for training stability and learning capacity. Across benchmarks and under matched compute, Tenma achieves an average success rate of 88.95% in-distribution and maintains strong performance under object and scene shifts, substantially exceeding baseline policies whose best in-distribution average is 18.12%. Despite using moderate data scale, Tenma delivers robust manipulation and generalization, indicating the great potential for multimodal and cross-embodiment learning strategies for further augmenting the capacity of transformer-based imitation learning policies. |
| title | Tenma: Robust Cross-Embodiment Robot Manipulation with Diffusion Transformer |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.11865 |