dGrasp: NeRF-Informed Implicit Grasp Policies with Supervised Optimization Slopes
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Accesso online: | |
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| _version_ | 1866913561240928256 |
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| author | Sóti, Gergely Huang, Xi Wurll, Christian Hein, Björn |
| author_facet | Sóti, Gergely Huang, Xi Wurll, Christian Hein, Björn |
| contents | We present dGrasp, an implicit grasp policy with an enhanced optimization landscape. This landscape is defined by a NeRF-informed grasp value function. The neural network representing this function is trained on simulated grasp demonstrations. During training, we use an auxiliary loss to guide not only the weight updates of this network but also the update how the slope of the optimization landscape changes. This loss is computed on the demonstrated grasp trajectory and the gradients of the landscape. With second order optimization, we incorporate valuable information from the trajectory as well as facilitate the optimization process of the implicit policy. Experiments demonstrate that employing this auxiliary loss improves policies' performance in simulation as well as their zero-shot transfer to the real-world. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09939 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | dGrasp: NeRF-Informed Implicit Grasp Policies with Supervised Optimization Slopes Sóti, Gergely Huang, Xi Wurll, Christian Hein, Björn Robotics We present dGrasp, an implicit grasp policy with an enhanced optimization landscape. This landscape is defined by a NeRF-informed grasp value function. The neural network representing this function is trained on simulated grasp demonstrations. During training, we use an auxiliary loss to guide not only the weight updates of this network but also the update how the slope of the optimization landscape changes. This loss is computed on the demonstrated grasp trajectory and the gradients of the landscape. With second order optimization, we incorporate valuable information from the trajectory as well as facilitate the optimization process of the implicit policy. Experiments demonstrate that employing this auxiliary loss improves policies' performance in simulation as well as their zero-shot transfer to the real-world. |
| title | dGrasp: NeRF-Informed Implicit Grasp Policies with Supervised Optimization Slopes |
| topic | Robotics |
| url | https://arxiv.org/abs/2406.09939 |