SQ-CBF: Signed Distance Functions for Numerically Stable Superquadric-Based Safety Filtering

TL;DR

This paper introduces SQ-CBF, a novel safety filtering method using signed distance functions for superquadrics, improving numerical stability and collision avoidance in complex, cluttered environments.

Contribution

It proposes a new safety filtering framework that overcomes ill-conditioning issues of implicit superquadric functions by using signed distance functions and randomized smoothing.

Findings

Demonstrates consistent collision-free manipulation in cluttered scenes

Shows robustness to noise and dynamic disturbances

Improves task efficiency in teleoperation

Abstract

Ensuring safe robot operation in cluttered and dynamic environments remains a fundamental challenge. While control barrier functions provide an effective framework for real-time safety filtering, their performance critically depends on the underlying geometric representation, which is often simplified, leading to either overly conservative behavior or insufficient collision coverage. Superquadrics offer an expressive way to model complex shapes using a few primitives and are increasingly used for robot safety. To integrate this representation into collision avoidance, most existing approaches directly use their implicit functions as barrier candidates. However, we identify a critical but overlooked issue in this practice: the gradients of the implicit SQ function can become severely ill-conditioned, potentially rendering the optimization infeasible and undermining reliable real-time safety filtering. To address this issue, we formulate an SQ-based safety filtering framework that uses signed distance functions as barrier candidates. Since analytical SDFs are unavailable for general SQs, we compute distances using the efficient Gilbert-Johnson-Keerthi algorithm and obtain gradients via randomized smoothing. Extensive simulation and real-world experiments demonstrate consistent collision-free manipulation in cluttered and unstructured scenes, showing robustness to challenging geometries, sensing noise, and dynamic disturbances, while improving task efficiency in teleoperation tasks. These results highlight a pathway toward safety filters that remain precise and reliable under the geometric complexity of real-world environments.