Clutter-Aware Spill-Free Liquid Transport via Learned Dynamics
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911974444498944 |
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| author | Abderezaei, Ava Pasricha, Anuj Klausenstock, Alex Roncone, Alessandro |
| author_facet | Abderezaei, Ava Pasricha, Anuj Klausenstock, Alex Roncone, Alessandro |
| contents | In this work, we present a novel algorithm to perform spill-free handling of open-top liquid-filled containers that operates in cluttered environments. By allowing liquid-filled containers to be tilted at higher angles and enabling motion along all axes of end-effector orientation, our work extends the reachable space and enhances maneuverability around obstacles, broadening the range of feasible scenarios. Our key contributions include: i) generating spill-free paths through the use of RRT* with an informed sampler that leverages container properties to avoid spill-inducing states (such as an upside-down container), ii) parameterizing the resulting path to generate spill-free trajectories through the implementation of a time parameterization algorithm, coupled with a transformer-based machine-learning model capable of classifying trajectories as spill-free or not. We validate our approach in real-world, obstacle-rich task settings using containers of various shapes and fill levels and demonstrate an extended solution space that is at least 3x larger than an existing approach. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_00215 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Clutter-Aware Spill-Free Liquid Transport via Learned Dynamics Abderezaei, Ava Pasricha, Anuj Klausenstock, Alex Roncone, Alessandro Robotics In this work, we present a novel algorithm to perform spill-free handling of open-top liquid-filled containers that operates in cluttered environments. By allowing liquid-filled containers to be tilted at higher angles and enabling motion along all axes of end-effector orientation, our work extends the reachable space and enhances maneuverability around obstacles, broadening the range of feasible scenarios. Our key contributions include: i) generating spill-free paths through the use of RRT* with an informed sampler that leverages container properties to avoid spill-inducing states (such as an upside-down container), ii) parameterizing the resulting path to generate spill-free trajectories through the implementation of a time parameterization algorithm, coupled with a transformer-based machine-learning model capable of classifying trajectories as spill-free or not. We validate our approach in real-world, obstacle-rich task settings using containers of various shapes and fill levels and demonstrate an extended solution space that is at least 3x larger than an existing approach. |
| title | Clutter-Aware Spill-Free Liquid Transport via Learned Dynamics |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.00215 |