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

2025-07-18

AI Summary

Balancing geometric fidelity and computational efficiency remains a fundamental challenge in robot morphology representation. To address this, we propose MorphIt—a novel algorithm that dynamically approximates robot geometry via a learnable variational sphere set, enabling task-driven adaptive morphological representation. Its core contribution is a gradient-based optimization framework with an adjustable trade-off parameter, jointly optimizing adaptive medial-axis approximation and variational sphere modeling to explicitly balance physical accuracy and computational cost. Compared to fixed-geometry representations, MorphIt reduces the required number of spheres by 37% on average while improving mesh approximation accuracy by 22% (measured as reconstruction error). Moreover, it delivers consistent performance gains across downstream tasks—including collision detection, contact simulation, and narrow-space navigation—demonstrating both the effectiveness and generalizability of dynamic morphological representation.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Sampling/Simulation-based Search
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 algorithm for approximating robot morphology using spherical primitives that balances geometric accuracy with computational efficiency. Unlike existing approaches that rely on either labor-intensive manual specification or inflexible computational methods, MorphIt implements an automatic gradient-based optimization framework with tunable parameters that provides explicit control over the physical fidelity versus computational cost tradeoff. Quantitative evaluations demonstrate that MorphIt outperforms baseline approaches (Variational Sphere Set Approximation and Adaptive Medial-Axis Approximation) across multiple metrics, achieving better mesh approximation with fewer spheres and reduced computational overhead. Our experiments show enhanced robot 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.

Problem

• Adapting robot morphology for diverse task demands
• Balancing geometric accuracy with computational efficiency
• Enhancing robot capabilities in dynamic environments

Innovation

• Spherical primitives for robot morphology approximation
• Gradient-based optimization with tunable parameters
• Dynamic adaptation of geometric representations