Force Generative Imitation Learning: Bridging Position Trajectory and Force Commands through Control Technique
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| Format: | Preprint |
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2026
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| _version_ | 1866914320812605440 |
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| author | Sato, Hiroshi Sakaino, Sho Tsuji, Toshiaki |
| author_facet | Sato, Hiroshi Sakaino, Sho Tsuji, Toshiaki |
| contents | In contact-rich tasks, while position trajectories are often easy to obtain, appropriate force commands are typically unknown. Although it is conceivable to generate force commands using a pretrained foundation model such as Vision-Language-Action (VLA) models, force control is highly dependent on the specific hardware of the robot, which makes the application of such models challenging. To bridge this gap, we propose a force generative model that estimates force commands from given position trajectories. However, when dealing with unseen position trajectories, the model struggles to generate accurate force commands. To address this, we introduce a feedback control mechanism. Our experiments reveal that feedback control does not converge when the force generative model has memory. We therefore adopt a model without memory, enabling stable feedback control. This approach allows the system to generate force commands effectively, even for unseen position trajectories, improving generalization for real-world robot writing tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06620 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Force Generative Imitation Learning: Bridging Position Trajectory and Force Commands through Control Technique Sato, Hiroshi Sakaino, Sho Tsuji, Toshiaki Robotics Systems and Control In contact-rich tasks, while position trajectories are often easy to obtain, appropriate force commands are typically unknown. Although it is conceivable to generate force commands using a pretrained foundation model such as Vision-Language-Action (VLA) models, force control is highly dependent on the specific hardware of the robot, which makes the application of such models challenging. To bridge this gap, we propose a force generative model that estimates force commands from given position trajectories. However, when dealing with unseen position trajectories, the model struggles to generate accurate force commands. To address this, we introduce a feedback control mechanism. Our experiments reveal that feedback control does not converge when the force generative model has memory. We therefore adopt a model without memory, enabling stable feedback control. This approach allows the system to generate force commands effectively, even for unseen position trajectories, improving generalization for real-world robot writing tasks. |
| title | Force Generative Imitation Learning: Bridging Position Trajectory and Force Commands through Control Technique |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2602.06620 |