MorphIt: Flexible Spherical Approximation of Robot Morphology for Representation-driven Adaptation

TL;DR

MorphIt introduces a flexible spherical approximation framework that enables robots to adapt their morphological representations for diverse tasks, improving efficiency and geometric fidelity through gradient-based optimization.

Contribution

MorphIt provides a novel, tunable spherical approximation method allowing task-driven morphological adaptation with significantly faster computation and better accuracy than existing approaches.

Findings

MorphIt generates spherical approximations up to 100x faster than baseline methods.

It achieves superior geometric fidelity with fewer spheres compared to existing methods.

Enhanced collision detection and navigation capabilities in robotics applications.

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 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 while maintaining superior geometric fidelity. Quantitative evaluations demonstrate that MorphIt outperforms baseline approaches (Variational Sphere Set Approximation and Adaptive Medial-Axis Approximation), achieving 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.