Reward functions are at the heart of every reinforcement learning (RL) algorithm. In robotic grasping, rewards are often complex and manually engineered functions that do not rely on well-justified physical models from grasp analysis. This work demonstrates that analytic grasp stability metrics constitute powerful optimization objectives for RL algorithms that refine grasps on a three-fingered hand using only tactile and joint position information. We outperform a binary-reward baseline by 42.9% and find that a combination of geometric and force-agnostic grasp stability metrics yields the highest average success rates of 95.4% for cuboids, 93.1% for cylinders, and 62.3% for spheres across wrist position errors between 0 and 7 centimeters and rotational errors between 0 and 14 degrees. In a second experiment, we show that grasp refinement algorithms trained with contact feedback (contact positions, normals, and forces) perform up to 6.6% better than a baseline that receives no tactile information.

中文翻译：

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使用深度强化学习和分析抓取稳定性指标的触觉抓取细化

奖励函数是每个强化学习 (RL) 算法的核心。在机器人抓取中，奖励通常是复杂且手动设计的功能，不依赖于抓取分析的合理物理模型。这项工作表明，分析抓取稳定性指标构成了 RL 算法的强大优化目标，该算法仅使用触觉和关节位置信息来优化三指手的抓取。我们的表现优于二元奖励基准 42.9%，并发现几何和力不可知的抓握稳定性指标的组合产生了最高的平均成功率，长方体为 95.4%，圆柱体为 93.1%，球体为 62.3%，跨越手腕位置错误0 到 7 厘米之间，旋转误差在 0 到 14 度之间。在第二个实验中，