SafeDMPs: Integrating Formal Safety with DMPs for Adaptive HRI
This addresses the problem of real-time, safe human-robot interaction by providing a closed-form solution that avoids computationally expensive optimization, though it is incremental in combining existing methods.
The paper tackles the challenge of combining robustness and formal safety in robots for human-centric environments by introducing SafeDMPs, a framework that integrates Dynamic Movement Primitives with a non-optimization-based control law, achieving orders of magnitude faster and more accurate performance than optimization-based baselines on a 7-DOF robot manipulator.
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.