Problem
Research questions and friction points this paper is trying to address.
Dynamic Movement Primitives
Formal Safety
Human-Robot Interaction
Control Barrier Functions
Real-time Control
SafeDMPs: Integrating Formal Safety with DMPs for Adaptive HRI
🤖 AI Summary
This work addresses the challenge of simultaneously achieving real-time performance and provably safe obstacle avoidance in human–robot interaction. It proposes the first integration of Dynamic Movement Primitives (DMPs) with a non-optimization-based safety control mechanism grounded in Spatio-Temporal Tubes, leveraging Control Barrier Functions (CBFs) to generate closed-form safe motions without online optimization. The resulting framework efficiently adapts to new goals while providing formal safety guarantees. Experimental validation on a 7-degree-of-freedom robotic arm demonstrates that the proposed method operates several orders of magnitude faster than optimization-based baselines, while also achieving higher trajectory accuracy—making it well-suited for real-time, safety-critical collaborative tasks.
📝 Abstract
Robots operating in human-centric environments must be both robust to disturbances and provably safe from collisions. Achieving these properties simultaneously and efficiently remains a central challenge. While Dynamic Movement Primitives (DMPs) offer inherent stability and generalization from single demonstrations, they lack formal safety guarantees. Conversely, formal methods like Control Barrier Functions (CBFs) provide provable safety but often rely on computationally expensive, real-time optimization, hindering their use in high-frequency control. This paper introduces SafeDMPs, a novel framework that resolves this trade-off. We integrate the closed-form efficiency and dynamic robustness of DMPs with a provably safe, non-optimization-based control law derived from Spatio-Temporal Tubes (STTs). This synergy allows us to generate motions that are not only robust to perturbations and adaptable to new goals, but also guaranteed to avoid static and dynamic obstacles. Our approach achieves a closed-form solution for a problem that traditionally requires online optimization. Experimental results on a 7-DOF robot manipulator demonstrate that SafeDMPs is orders of magnitude faster and more accurate than optimization-based baselines, making it an ideal solution for real-time, safe, and collaborative robotics.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic Movement Primitives
Control Barrier Functions
Spatio-Temporal Tubes
Formal Safety
Real-time Robotics
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