• Corpus ID: 8051413

Learning Robotic Manipulation of Granular Media

@inproceedings{Schenck2017LearningRM,
  title={Learning Robotic Manipulation of Granular Media},
  author={Connor Schenck and Jonathan Tompson and Sergey Levine and Dieter Fox},
  booktitle={Conference on Robot Learning},
  year={2017}
}
In this paper, we examine the problem of robotic manipulation of granular media. [] Key Method Our best performing model is based on a highly-tailored convolutional network architecture with domain-specific optimizations, which we show accurately models the physical interaction of the robotic scoop with the underlying media. We empirically demonstrate that explicitly predicting physical mechanics results in a policy that out-performs both a hand-crafted dynamics baseline, and a "value-network", which must…
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32 Citations
Highly Influential Citations
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Background Citations
24
Methods Citations
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32 Citations

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A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms

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ToolFlowNet: Robotic Manipulation with Tools via Predicting Tool Flow from Point Clouds

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Few-shot Adaptation for Manipulating Granular Materials Under Domain Shift

This paper proposes an adaptive scooping strategy that uses deep Gaussian process method trained with meta-learning to learn on-line from very limited experience on the target terrains, significantly outperforming non-adaptive methods proposed in the excavation literature as well as other state-of-the-art meta- learning methods.

Active Model Learning and Diverse Action Sampling for Task and Motion Planning

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Model-free vision-based shaping of deformable plastic materials

It is shown that it is possible to obtain simple shapes with the kinetic sand, without explicitly modeling the material, and a richer set of action types and multi-step reasoning is needed to achieve more sophisticated shapes.

Inferring the Material Properties of Granular Media for Robotic Tasks

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