基于构型空间神经符号距离函数的隐式关节机器人形态建模
Implicit Articulated Robot Morphology Modeling with Configuration Space Neural Signed Distance Functions
AI总结：
本文提出机器人神经距离函数RNDF，用构型空间符号距离函数隐式建模关节机器人形态，以更少参数实现更高精度，并应用于杂乱环境下的全臂无碰撞抓取规划。
摘要：
本文提出一种新颖方法，利用基于前向运动学的构型空间符号距离函数隐式编码精确的机器人形态。我们提出的机器人神经距离函数（RNDF）针对每个连杆以机器人构型为条件的符号距离查询，优化了计算效率与精度之间的平衡。与基线方法相比，所提方法在距离误差上降低了81.1%，同时仅使用47.6%的模型参数。其可并行化和可微分的特性提供了对关节空间导数的直接访问，使笛卡尔任务空间中的机器人规划与构型空间之间能够无缝连接。这些特性使RNDF成为通用机器人优化和3D空间规划任务中学习的理想代理模型。具体而言，我们将RNDF应用于机器人手臂-手部建模，并展示了其作为杂乱环境中全臂无碰撞抓取规划核心平台的潜力。代码和模型可在https://github.com/robotic-manipulation/RNDF获取。
英文摘要：
In this paper, we introduce a novel approach to implicitly encode precise robot morphology using forward kinematics based on a configuration space signed distance function. Our proposed Robot Neural Distance Function (RNDF) optimizes the balance between computational efficiency and accuracy for signed distance queries conditioned on the robot's configuration for each link. Compared to the baseline method, the proposed approach achieves an 81.1% reduction in distance error while utilizing only 47.6% of model parameters. Its parallelizable and differentiable nature provides direct access to joint-space derivatives, enabling a seamless connection between robot planning in Cartesian task space and configuration space. These features make RNDF an ideal surrogate model for general robot optimization and learning in 3D spatial planning tasks. Specifically, we apply RNDF to robotic arm-hand modeling and demonstrate its potential as a core platform for whole-arm, collision-free grasp planning in cluttered environments. The code and model are available at https://github.com/robotic-manipulation/RNDF.
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。
