用能量模型预测共用抓取点，提升机器人抓放规划效率
Learning from Planned Data to Improve Robotic Pick-and-Place Planning Efficiency
- 构建能量模型联合评估物体初始与目标位姿的可行抓取
- 减少搜索空间，提升抓取选择性能与数据效率
- 适合需高效抓放规划的工业机器人场景
本文提出一种学习方法，通过预测共用抓取点加速机器人抓放规划。共用抓取点指在抓取任务中同时适用于初始和目标物体姿态的抓取位姿。传统解析方法对抓取候选点分别评估，导致候选集增大时计算开销显著上升。为此，我们引入能量模型（EBM），通过组合物体在两个姿态下的可行抓取能量，实现共用抓取点的预测。该方法可提前识别有潜力的候选点，显著缩小搜索空间。实验表明，该方法在抓取选择性能、数据效率及对未见抓取和相似形状物体的泛化能力上均有提升。
原文摘要 · Abstract (English)
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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