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arXiv · 2109.10583

Efficient Object Manipulation to an Arbitrary Goal Pose: Learning-based Anytime Prioritized Planning

Kechun Xu·Hongxiang Yu·Renlang Huang·Dashun Guo·Yue Wang·Rong Xiong
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Abstract

We focus on the task of object manipulation to an arbitrary goal pose, in which a robot is supposed to pick an assigned object to place at the goal position with a specific orientation. However, limited by the execution space of the manipulator with gripper, one-step picking, moving and releasing might be failed, where a reorientation object pose is required as a transition. In this paper, we propose a learning-driven anytime prioritized search-based solver to find a feasible solution with low path cost in a short time. In our work, the problem is formulated as a hierarchical learning problem, with the high level finding a reorientation object pose, and the low level planning paths between adjacent grasps. We learn an offline-training path cost estimator to predict approximate path planning costs, which serve as pseudo rewards to allow for pre-training the high-level planner without interacting with the simulator. To deal with the problem of distribution mismatch of the cost net and the actual execution cost space, a refined training stage is conducted with simulation interaction. A series of experiments carried out in simulation and real world indicate that our system can achieve better performances in the object manipulation task with less time and less cost.

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Kechun Xu, Hongxiang Yu, Renlang Huang, Dashun Guo, Yue Wang, Rong Xiong. 2021-09-22. Efficient Object Manipulation to an Arbitrary Goal Pose: Learning-based Anytime Prioritized Planning. https://arxiv.org/abs/2109.10583

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