用贝叶斯优化让机器人用手感传感器安全抓取未知物体
Bayesian optimization for robust robotic grasping using a sensorized compliant hand
- 通过贝叶斯优化主动学习，结合触觉反馈优化抓取策略
- 在真实环境中实现对未知物体的稳定抓取，抗噪声干扰能力强
- 适合需要自适应抓取的工业或助人机器人场景
人类从小便依靠触觉感知学会抓取物体。将此能力赋予机器人可提升工业柔性或辅助肢体障碍者。然而，面对多样且未知的物体时，传统试错法效率低下。本文提出利用贝叶斯优化技术，在真实机器人系统中结合触觉传感器，通过分析不同抓取评估指标，实现安全、鲁棒的抓取优化。实验表明，该方法能在存在噪声与不确定性的现实环境中，有效完成未知物体的抓取任务。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。