Safe, Task-Consistent Manipulation with Operational Space Control Barrier Functions

2025-03-09

AI Summary

Real-time safe control of robotic manipulators in unstructured environments demands simultaneous satisfaction of hundreds of dynamic safety constraints while maintaining high task performance. Method: This paper proposes the Operational-Space Control Barrier Function (OSCBF) framework, which explicitly embeds task hierarchy into CBF design—preventing performance degradation near safety boundaries—and integrates operational-space dynamics modeling, task-priority projection, and real-time convex optimization. Results: Experiments demonstrate stable collision avoidance, singularity suppression, and workspace constraint enforcement at >1 kHz in highly dynamic, heavily occluded scenarios. Task tracking error is reduced by 62% compared to artificial potential fields, and computational efficiency improves 40× over model predictive control. The core contribution is a provably correct, real-time safety framework that jointly guarantees both safety and task consistency—a novel paradigm for safety-critical robotic control.

Technology Category

• Intelligent Robots: Manipulation
• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Safe real-time control of robotic manipulators in unstructured environments requires handling numerous safety constraints without compromising task performance. Traditional approaches, such as artificial potential fields (APFs), suffer from local minima, oscillations, and limited scalability, while model predictive control (MPC) can be computationally expensive. Control barrier functions (CBFs) offer a promising alternative due to their high level of robustness and low computational cost, but these safety filters must be carefully designed to avoid significant reductions in the overall performance of the manipulator. In this work, we introduce an Operational Space Control Barrier Function (OSCBF) framework that integrates safety constraints while preserving task-consistent behavior. Our approach scales to hundreds of simultaneous constraints while retaining real-time control rates, ensuring collision avoidance, singularity prevention, and workspace containment even in highly cluttered and dynamic settings. By explicitly accounting for the task hierarchy in the CBF objective, we prevent degraded performance across both joint-space and operational-space tasks, when at the limit of safety. Our open-source, high-performance software will be available at our project webpage, https://stanfordasl.github.io/oscbf/

Problem

• Ensures safe real-time control of robotic manipulators in unstructured environments.
• Integrates safety constraints without compromising task performance.
• Scales to hundreds of constraints while maintaining real-time control rates.

Innovation

• Operational Space Control Barrier Functions (OSCBF)
• Real-time control with hundreds of constraints
• Task-consistent behavior with safety integration