论文/arXiv 2507.14061·9 天前

MORPHIT：面向表示驱动适应的机器人形态灵活球形近似

MORPHIT通过梯度优化自动调节机器人形态的球形近似，并在定量评估和机器人基础设施集成中验证。

AI 整理

• 本体 SDK 与控制
• 感知与导航
• 仿真与训练

原文链接：MORPHIT: Flexible spherical approximation of robot morphology for representation-driven adaptation（arXiv 2507.14061 · 发布日期：2026-10-19）

MORPHIT: Flexible spherical approximation of robot morphology for representation-driven adaptation

MORPHIT通过梯度优化自动调节机器人形态的球形近似，并在定量评估和机器人基础设施集成中验证。

核心要点

• 现有机器人常将形态表示视为固定约束，难以兼顾不同任务的计算与精度需求。
• MORPHIT将形态表示作为可调资源，用梯度优化实现任务驱动的自动适应。
• 定量评估显示其优于基线，以更少球体获得更好网格近似，并较优化方法最高快100倍。
• 集成现有机器人基础设施后，可增强碰撞检测、接触丰富交互仿真和狭窄空间导航能力。

验证范围与局限：验证范围包括定量评估、接触丰富交互仿真及狭窄空间导航，摘要未说明真机测试。

英文摘要 (Abstract)

What if a robot could rethink its own morphological representation to better meet the demands of diverse tasks? Most robotic systems today treat their physical form as a fixed constraint rather than an adaptive resource, forcing the same rigid geometric representation to serve applications with vastly different computational and precision requirements. We introduce morphit , a novel spherical approximation framework that treats morphological representation as a tunable resource. morphit enables automatic task-driven morphological adaptation through gradient-based optimization with tunable parameters that provide explicit control over the accuracy-efficiency tradeoff. Unlike existing approaches that rely on either labor-intensive manual specification or inflexible computational methods optimized for visualization rather than robotics, morphit generates spherical approximations up to 100x faster than optimization-based methods while maintaining superior geometric fidelity. Quantitative evaluations demonstrate that morphit outperforms baseline approaches and achieves better mesh approximation with fewer spheres. Through seamless integration with existing robotics infrastructure, morphit enables enhanced capabilities in collision detection accuracy, contact-rich interaction simulation, and navigation through confined spaces. By dynamically adapting geometric representations to task requirements, robots can now exploit their physical embodiment as an active resource rather than an inflexible parameter, opening new frontiers for manipulation in environments where physical form must continuously balance precision with computational tractability.

作者：Nataliya Nechyporenko, Yutong Zhang, Sean Campbell, Alessandro Roncone

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2507.14061：MORPHIT: Flexible spherical approximation of robot morphology for representation-driven adaptation访问 2026-10-21