用人类手部动作先验提升机器人对物体运动结构的实时估计精度
A Helping (Human) Hand in Kinematic Structure Estimation
- 以人类手部运动为先验，结合视觉不确定性建模
- 在遮挡和纹理缺失场景下，性能优于基线195%和140%
- 可支持机器人安全操作小型复杂物体，适合人机协作场景
视觉不确定性（如遮挡、缺乏纹理、噪声）给安全机器人操作中获取准确运动模型带来挑战。本文提出一种概率性实时方法，利用人类手部运动作为先验来缓解这些不确定性。通过追踪操作过程中人类手部的约束运动，并显式建模视觉观测中的不确定性，该方法能够在线可靠地估计物体的运动结构模型。我们在一个包含复杂遮挡与有限可感知活动度物体的新数据集上验证了该方法。结果表明，通过引入合适先验并显式处理不确定性，该方法显著提升了估计精度，在两个近期基线上分别实现了195%和140%的性能提升。此外，我们证明该方法的估计足够精确，可使机器人安全操作小型物体。
原文摘要 · Abstract (English)
Visual uncertainties such as occlusions, lack of texture, and noise present significant challenges in obtaining accurate kinematic models for safe robotic manipulation. We introduce a probabilistic real-time approach that leverages the human hand as a prior to mitigate these uncertainties. By tracking the constrained motion of the human hand during manipulation and explicitly modeling uncertainties in visual observations, our method reliably estimates an object's kinematic model online. We validate our approach on a novel dataset featuring challenging objects that are occluded during manipulation and offer limited articulations for perception. The results demonstrate that by incorporating an appropriate prior and explicitly accounting for uncertainties, our method produces accurate estimates, outperforming two recent baselines by 195% and 140%, respectively. Furthermore, we demonstrate that our approach's estimates are precise enough to allow a robot to manipulate even small objects safely.
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。