使用传感器化柔顺手实现鲁棒机器人抓取的贝叶斯优化
Bayesian optimization for robust robotic grasping using a sensorized compliant hand
AI总结：
针对机器人在噪声与不确定性环境中抓取未知物体的问题，本文利用贝叶斯优化和触觉抓取指标，在传感器化柔顺手上实现安全、鲁棒的真实抓取。
摘要：
我们童年时期最早学会的任务之一，就是基于触觉感知抓取物体。将这种技能融入机器人将支持多种应用，例如提高工业流程的灵活性，或为身体残障人士提供辅助。然而，困难在于使抓取策略适应种类繁多的任务和物体，而这些任务和物体通常可能是未知的。暴力解决方案是通过反复试错来学习新的抓取方式，但这种方法效率低下且效果不佳。相比之下，贝叶斯优化通过向最优抓取的近似中添加信息来实施主动学习。本文提出使用贝叶斯优化技术安全地执行机器人抓取。我们分析了不同的抓取指标，以便在包含触觉传感器的真实系统中提供符合实际的抓取优化。在机器人系统中的实验评估表明，即使存在真实环境固有的噪声和不确定性，该方法对于执行未知物体抓取仍然具有实用性。
英文摘要：
One of the first tasks we learn as children is to grasp objects based on our tactile perception. Incorporating such skill in robots will enable multiple applications, such as increasing flexibility in industrial processes or providing assistance to people with physical disabilities. However, the difficulty lies in adapting the grasping strategies to a large variety of tasks and objects, which can often be unknown. The brute-force solution is to learn new grasps by trial and error, which is inefficient and ineffective. In contrast, Bayesian optimization applies active learning by adding information to the approximation of an optimal grasp. This paper proposes the use of Bayesian optimization techniques to safely perform robotic grasping. We analyze different grasp metrics to provide realistic grasp optimization in a real system including tactile sensors. An experimental evaluation in the robotic system shows the usefulness of the method for performing unknown object grasping even in the presence of noise and uncertainty inherent to a real-world environment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
