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@inproceedings{Wada:2022:10.1109/icra46639.2022.9811881,
author = {Wada, K and James, S and Davison, AJ},
doi = {10.1109/icra46639.2022.9811881},
publisher = {IEEE},
title = {ReorientBot: learning object reorientation for specific-posed placement},
url = {http://dx.doi.org/10.1109/icra46639.2022.9811881},
year = {2022}
}

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TY  - CPAPER
AB  - Robots need the capability of placing objects in arbitrary, specific poses to rearrange the world and achieve various valuable tasks. Object reorientation plays a crucial role in this as objects may not initially be oriented such that the robot can grasp and then immediately place them in a specific goal pose. In this work, we present a vision-based manipulation system, ReorientBot, which consists of 1) visual scene understanding with pose estimation and volumetric reconstruction using an onboard RGB-D camera; 2) learned waypoint selection for successful and efficient motion generation for reorientation; 3) traditional motion planning to generate a collision-free trajectory from the selected waypoints. We evaluate our method using the YCB objects in both simulation and the real world, achieving 93% overall success, 81% improvement in success rate, and 22% improvement in execution time compared to a heuristic approach. We demonstrate extended multi-object rearrangement showing the general capability of the system.
AU  - Wada,K
AU  - James,S
AU  - Davison,AJ
DO  - 10.1109/icra46639.2022.9811881
PB  - IEEE
PY  - 2022///
TI  - ReorientBot: learning object reorientation for specific-posed placement
UR  - http://dx.doi.org/10.1109/icra46639.2022.9811881
UR  - https://ieeexplore.ieee.org/document/9811881
UR  - http://hdl.handle.net/10044/1/99512
ER  -

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