ContactSDF：作为灵巧操作多接触模型的有符号距离函数
ContactSDF: Signed Distance Functions as Multi-Contact Models for Dexterous Manipulation
AI总结：
本研究提出ContactSDF方法，通过两个有符号距离函数构建可微多接触动力学模型，实现灵巧操作的高效模型学习与控制，经仿真与硬件实验验证，短时间学习即可达成高频高质量操作效果。
摘要：
本文提出ContactSDF方法，该方法利用有符号距离函数（SDF）近似多接触模型，涵盖碰撞检测与时间步进例程两部分。ContactSDF首先借助物体的支撑平面表示构建SDF用于碰撞检测，再利用生成的接触对偶锥建立第二个SDF，用于下一状态的时间步进预测。这两个SDF共同构成了可微且具有解析形式的多接触动力学模型以进行状态预测，为接触丰富的操作任务提供了高效的模型学习与优化途径。我们开展了大量仿真实验，验证了ContactSDF在灵巧操作的模型学习与实时控制方面的有效性。我们进一步在硬件Allegro手上针对掌内重定向任务评估了ContactSDF。结果表明，仅需在硬件上进行约2分钟的学习，ContactSDF就能以30-60Hz的频率实现高质量的灵巧操作。项目页面：https://yangwen-1102.github.io/contactsdf.github.io/
英文摘要：
In this paper, we propose ContactSDF, a method that uses signed distance functions (SDFs) to approximate multi-contact models, including both collision detection and time-stepping routines. ContactSDF first establishes an SDF using the supporting plane representation of an object for collision detection, and then uses the generated contact dual cones to build a second SDF for time-stepping prediction of the next state. Those two SDFs create a differentiable and closed-form multi-contact dynamic model for state prediction, enabling efficient model learning and optimization for contact-rich manipulation. We perform extensive simulation experiments to show the effectiveness of ContactSDF for model learning and real-time control of dexterous manipulation. We further evaluate the ContactSDF on a hardware Allegro hand for on-palm reorientation tasks. Results show with around 2 minutes of learning on hardware, the ContactSDF achieves high-quality dexterous manipulation at a frequency of 30-60Hz. Project page https://yangwen-1102.github.io/contactsdf.github.io/
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。
