FetchBench: A Simulation Benchmark for Robot Fetching
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913552342712320 |
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| author | Han, Beining Parakh, Meenal Geng, Derek Defay, Jack A Luyang, Gan Deng, Jia |
| author_facet | Han, Beining Parakh, Meenal Geng, Derek Defay, Jack A Luyang, Gan Deng, Jia |
| contents | Fetching, which includes approaching, grasping, and retrieving, is a critical challenge for robot manipulation tasks. Existing methods primarily focus on table-top scenarios, which do not adequately capture the complexities of environments where both grasping and planning are essential. To address this gap, we propose a new benchmark FetchBench, featuring diverse procedural scenes that integrate both grasping and motion planning challenges. Additionally, FetchBench includes a data generation pipeline that collects successful fetch trajectories for use in imitation learning methods. We implement multiple baselines from the traditional sense-plan-act pipeline to end-to-end behavior models. Our empirical analysis reveals that these methods achieve a maximum success rate of only 20%, indicating substantial room for improvement. Additionally, we identify key bottlenecks within the sense-plan-act pipeline and make recommendations based on the systematic analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_11793 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FetchBench: A Simulation Benchmark for Robot Fetching Han, Beining Parakh, Meenal Geng, Derek Defay, Jack A Luyang, Gan Deng, Jia Robotics Fetching, which includes approaching, grasping, and retrieving, is a critical challenge for robot manipulation tasks. Existing methods primarily focus on table-top scenarios, which do not adequately capture the complexities of environments where both grasping and planning are essential. To address this gap, we propose a new benchmark FetchBench, featuring diverse procedural scenes that integrate both grasping and motion planning challenges. Additionally, FetchBench includes a data generation pipeline that collects successful fetch trajectories for use in imitation learning methods. We implement multiple baselines from the traditional sense-plan-act pipeline to end-to-end behavior models. Our empirical analysis reveals that these methods achieve a maximum success rate of only 20%, indicating substantial room for improvement. Additionally, we identify key bottlenecks within the sense-plan-act pipeline and make recommendations based on the systematic analysis. |
| title | FetchBench: A Simulation Benchmark for Robot Fetching |
| topic | Robotics |
| url | https://arxiv.org/abs/2406.11793 |