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

• Published in: 2022 International Conference on Robotics and Automation (ICRA) pp. 7277 - 7283
• Main Authors: Xu, Kechun, Yu, Hongxiang, Huang, Renlang, Guo, Dashun, Wang, Yue, Xiong, Rong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23.05.2022
• Subjects: Automation; Costs; Deep learning; Manipulators; Path planning; Planning; Training
• DOI: 10.1109/ICRA46639.2022.9811547

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.