Generative Factor Chaining: Coordinated Manipulation with Diffusion-based Factor Graph
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913515877433344 |
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| author | Mishra, Utkarsh A. Chen, Yongxin Xu, Danfei |
| author_facet | Mishra, Utkarsh A. Chen, Yongxin Xu, Danfei |
| contents | Learning to plan for multi-step, multi-manipulator tasks is notoriously difficult because of the large search space and the complex constraint satisfaction problems. We present Generative Factor Chaining~(GFC), a composable generative model for planning. GFC represents a planning problem as a spatial-temporal factor graph, where nodes represent objects and robots in the scene, spatial factors capture the distributions of valid relationships among nodes, and temporal factors represent the distributions of skill transitions. Each factor is implemented as a modular diffusion model, which are composed during inference to generate feasible long-horizon plans through bi-directional message passing. We show that GFC can solve complex bimanual manipulation tasks and exhibits strong generalization to unseen planning tasks with novel combinations of objects and constraints. More details can be found at: https://generative-fc.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16275 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative Factor Chaining: Coordinated Manipulation with Diffusion-based Factor Graph Mishra, Utkarsh A. Chen, Yongxin Xu, Danfei Robotics Learning to plan for multi-step, multi-manipulator tasks is notoriously difficult because of the large search space and the complex constraint satisfaction problems. We present Generative Factor Chaining~(GFC), a composable generative model for planning. GFC represents a planning problem as a spatial-temporal factor graph, where nodes represent objects and robots in the scene, spatial factors capture the distributions of valid relationships among nodes, and temporal factors represent the distributions of skill transitions. Each factor is implemented as a modular diffusion model, which are composed during inference to generate feasible long-horizon plans through bi-directional message passing. We show that GFC can solve complex bimanual manipulation tasks and exhibits strong generalization to unseen planning tasks with novel combinations of objects and constraints. More details can be found at: https://generative-fc.github.io/ |
| title | Generative Factor Chaining: Coordinated Manipulation with Diffusion-based Factor Graph |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.16275 |