通过精细粒度接触生成和自然姿势优化进行敏捷的抓取合成
Meng Ning1,2, Chong Deng1, Ziheng Zhan1,2
1School of Intelligent Manufacturing, Jiangnan University, Wuxi 214122, China.
Biomimetics (Basel, Switzerland)
|August 27, 2025
概括
通过提高接触地图的准确性和产生更自然,更可信的抓取手势, Diffangle-Grasp 改进了人类形状的机器人抓取. 这导致机器人操纵任务的成功率更高,错误率更低.
科学领域:
- 机器人技术
- 人工智能
- 计算机视觉
背景情况:
- 准确的接触地图和自然的抓取手势对于人形机器人抓取至关重要.
- 目前的方法在掌握生成准确性和手势理性方面面临挑战.
研究的目的:
- 提出一个改进的掌握生成方案,Diffangle-Grasp,解决接触地图的准确性和掌握手势的自然性.
- 提高人类形状的机器人抓捕系统的性能.
主要方法:
- 开发了Diffangle-Grasp,一个两部分方案,包括条件变量自编码器 (CVAE) 来生成接触地图和扩散模型以优化抓取.
- 确保产生的握力符合物理规律和自然姿势.
主要成果:
- 与基础模型相比,减少了9.59%的接触地图重建损失.
- 增加了2.15%的自然感和3.27%的成功率.
- 在保持抓取模拟位移的同时,透体积减少了11.06%.
结论:
- 不同角度抓取有效地增强了人形机器人的抓取精度和手势自然性.
- 拟议的方法显示了与现有方案相比的显著改进,并为人机掌握提供了技术可行性.
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