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arXiv · 2506.15920

Learning from Planned Data to Improve Robotic Pick-and-Place Planning Efficiency

Liang Qin·Weiwei Wan·Jun Takahashi·Ryo Negishi·Masaki Matsushita·Kensuke Harada
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Abstract

This work proposes a learning method to accelerate robotic pick-and-place planning by predicting shared grasps. Shared grasps are defined as grasp poses feasible to both the initial and goal object configurations in a pick-and-place task. Traditional analytical methods for solving shared grasps evaluate grasp candidates separately, leading to substantial computational overhead as the candidate set grows. To overcome the limitation, we introduce an Energy-Based Model (EBM) that predicts shared grasps by combining the energies of feasible grasps at both object poses. This formulation enables early identification of promising candidates and significantly reduces the search space. Experiments show that our method improves grasp selection performance, offers higher data efficiency, and generalizes well to unseen grasps and similarly shaped objects.

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Liang Qin, Weiwei Wan, Jun Takahashi, Ryo Negishi, Masaki Matsushita, Kensuke Harada. 2025-06-18. Learning from Planned Data to Improve Robotic Pick-and-Place Planning Efficiency. https://arxiv.org/abs/2506.15920

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