SafeDMPs: Integrating Formal Safety with DMPs for Adaptive HRI

TL;DR

SafeDMPs combines the stability and adaptability of DMPs with formal safety guarantees from STTs, enabling real-time, collision-free robot motion in human environments.

Contribution

It introduces a non-optimization-based control law integrating DMPs with STTs for provably safe, efficient, and adaptable robot motion planning.

Findings

SafeDMPs is significantly faster than optimization-based methods.

The approach guarantees collision avoidance with static and dynamic obstacles.

Experimental validation on a 7-DOF robot demonstrates real-time performance.

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.