Learning Distributional Demonstration Spaces for Task-Specific Cross-Pose Estimation
<|reference_start|>Learning Distributional Demonstration Spaces for Task-Specific Cross-Pose Estimation: Relative placement tasks are an important category of tasks in which one object needs to be placed in a desired pose relative to another object. Previous work has shown success in learning relative placement tasks from just a small number of demonstrations when using relational reasoning networks with geometric inductive biases. However, such methods cannot flexibly represent multimodal tasks, like a mug hanging on any of n racks. We propose a method that incorporates additional properties that enable learning multimodal relative placement solutions, while retaining the provably translation-invariant and relational properties of prior work. We show that our method is able to learn precise relative placement tasks with only 10-20 multimodal demonstrations with no human annotations across a diverse set of objects within a category.<|reference_end|>
@article{wang2024learning,
    title={Learning Distributional Demonstration Spaces for Task-Specific
  Cross-Pose Estimation},
    author={Jenny Wang, Octavian Donca, David Held},
    journal={arXiv preprint arXiv:2405.04609},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04609},
    primaryClass={cs.RO}
}