神经感觉与神经场:用于手中的操纵视觉触觉感知
Sudharshan Suresh1,2, Haozhi Qi2,3, Tingfan Wu2
1Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Science robotics
|November 13, 2024
概括
机器人现在可以通过将视觉和触觉结合起来,在操纵过程中更好地理解物体. 这种多式传感提高了空间意识,特别是在视力受阻时,提高了机器人的灵巧度.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 传感器融合式传感器
背景情况:
- 人类水平的机器人灵敏度需要复杂的空间意识,特别是用于手动操纵新型对象.
- 当前手持感知系统主要依赖视觉,限制其有效性与封闭的物体和先验未知的对象模型.
- 视觉阻塞是机器人操纵的一个重大挑战,阻碍了强大的手动操纵能力的发展.
研究的目的:
- 开发一种多式传感方法,将视觉和触觉结合起来,在手操作过程中估计物体的姿势和形状.
- 克服仅视觉系统的局限性,特别是在视觉阻塞严重的场景中.
- 通过创建一个强大的感知脊柱来提高机器人的灵巧性,用于手中的物体交互.
主要方法:
- 介绍了NeuralFeels,一种使用在线学习的神经场编码对象几何学的方法.
- 通过优化姿势图问题,整合视觉和触摸数据,共同追踪对象的姿势和形状.
- 在模拟和现实世界的实验中,利用以自身感知为驱动的策略与对象进行交互.
主要成果:
- 实现了对象几何学的最终重建F-分数为81%.
- 展示了4.7毫米的平均姿势漂移,在已知的物体模型中减少到2.3毫米.
- 在严重的视觉阻塞下显示出显著的跟踪改进 (高达94%与仅视觉方法相比).
结论:
- 多式传感,将触觉与视觉结合起来,在手动操纵过程中精细化和明确化对象感知.
- 神经感觉方法为推进机器人灵巧性提供了强大的感知骨干,特别是在具有挑战性的阻塞场景中.
- 释放FeelSight数据集的目的是促进对比测试和进一步研究多式联络手感知.
相关概念视频
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